{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:56:37.627703Z",
     "start_time": "2025-06-10T02:56:37.470407Z"
    }
   },
   "source": [
    "import numpy as np \n",
    "import math \n",
    "import pandas as pd \n",
    "pd.set_option('display.float_format',lambda x:'%.3f' % x)\n",
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "sns.set_palette('muted')\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "import score_card as sc\n",
    "import missingno\n",
    "os.chdir('F:/安装包/PPD-First-Round-Data-Updated/PPD-First-Round-Data-Update/')"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:56:41.181548Z",
     "start_time": "2025-06-10T02:56:40.315853Z"
    }
   },
   "source": [
    "# 导入 data_input 处理好的数据\n",
    "df1 = pd.read_csv('data_input1.csv',encoding='gb18030')"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:56:41.933503Z",
     "start_time": "2025-06-10T02:56:41.925600Z"
    }
   },
   "source": [
    "df1.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49999, 229)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:56:43.313540Z",
     "start_time": "2025-06-10T02:56:43.306539Z"
    }
   },
   "source": [
    "# 样本的好坏比\n",
    "df1.target.value_counts()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0.000    27802\n",
       "1.000     2198\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 借款成交量的时间趋势变化 "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:23.677667Z",
     "start_time": "2025-06-10T02:57:22.596681Z"
    }
   },
   "source": [
    "# 借款成交时间的范围\n",
    "import datetime as dt\n",
    "df1['ListingInfo'] = pd.to_datetime(df1.ListingInfo,format='mixed')\n",
    "# 每个月份的用户数分布\n",
    "df1['month'] = df1.ListingInfo.dt.strftime('%Y-%m')\n",
    "# 绘制成交量的时间趋势图\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.title('借款成交量的时间趋势图')\n",
    "plt.rcParams['font.sans-serif']=['Microsoft YaHei']\n",
    "sns.countplot(data=df1.sort_values('month'),x='month')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 违约情况的时间趋势分析 "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:27.041446Z",
     "start_time": "2025-06-10T02:57:26.710549Z"
    }
   },
   "source": [
    "month_group = df1.groupby('month') # 根据月份计算每个月的违约率\n",
    "time_bad_trend = pd.DataFrame()\n",
    "time_bad_trend['total'] = month_group.target.count()\n",
    "time_bad_trend['bad'] = month_group.target.sum()\n",
    "time_bad_trend['bad_rate']=time_bad_trend['bad']/time_bad_trend['total']\n",
    "time_bad_trend = time_bad_trend.reset_index()\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.title('违约率的时间趋势图')\n",
    "sns.pointplot(data=time_bad_trend,x='month',y='bad_rate',linestyles='-')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/oAAAGQCAYAAAAeFhu0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACHP0lEQVR4nO3dd3gU5doG8HtLdje9kApJkARIKKEFAggoTUARFBQ4KCCIolgRj4hHbJQPj0csEFCkWEBBQVAUFAWkI0GQgFICSYCEFNJ7ts73R8wkm03f3ewmuX/XxaUz887ss5NN3n3mbRJBEAQQERERERERUYsgtXUARERERERERGQ5TPSJiIiIiIiIWhAm+kREREREREQtCBN9IiIiIiIiohaEiT4RERERERFRC8JEn4iIiIiIiKgFYaJPRERERERE1IIw0SciIiIiIiJqQZjoExFRq5OdnV1nmQ8//BDr16+v9zU/+eQTXLhwwWT/X3/9hU8//RQZGRkNirEqg8GAn3/+Gbdu3WrU+Z999hlOnDhR7bH8/HxER0cjLi7OnBBrtGbNGtx7773VHjMYDNi5cycEQbDoa/7yyy84efJkjcf++uuvBl1v6tSp+OSTT6o9dtddd+HHH39scIxERETWIrd1AERERE3p+++/xxtvvIFNmzYhIiKixnKHDh2Cp6cnHnvssTqv+ccff2DFihXQ6XTo2rWr0bHNmzfj+++/x6hRo8yK++rVq5g/fz7uuecevPvuuw06NzU1FW+//TbGjx+PgQMHmhz/7bffsGrVKgQEBKBz585mxVmdzMxMJCQkVHts27ZteP311/Hnn3/irbfegkQiwcWLF3Hx4sV6XbtNmza48847TfavXLkS4eHh6N+/v9H+mzdvYv78+ZgxYwa6d+9e7/eQnp6O/Pz8ao/duHEDBQUFdV5j4cKF2LlzZ71fszpTpkzB4sWLzboGERG1fEz0iYioVRkyZAhcXFzw/PPP47vvvoOrqyv27NljUi4/Px96vR67d++u9jrDhg2Dk5MTgLKk0t/fH48++qjJNfbu3YuhQ4eiXbt2tcaVmZmJY8eO1VomPDwcP/zwA7p16wYvL68ay/Xv3x/+/v7i9nfffQdBEDB9+vRqy+/duxeOjo4YPXp0ra/fv39/5Obmmuzv1q0bduzYUeu5NZkwYQJ27tyJr7/+GgDw1ltvYd++fYiOjq7X+X369Kk20a/JqlWroNVq8eWXX2LLli1Gxz788EPccccd9bqOIAjQaDTitl6vh1qtFrdlMhnkctOvWd7e3njmmWeM9hUUFEAul8PR0bHW1/zggw/qFRsRERETfSIialW8vLywbNkyzJkzB4sWLcJ7772H+fPn11i+pmP79++Hk5MTDh48iJMnT+K///0vVCqVUZmtW7eiuLgYDg4ONXb7dnFxwUMPPYT4+HgsWLCgXu/h7bffrvX46tWr4e/vj59++gklJSXYtm0bvL29ceXKFVy5cgUAEBwcjL59+yIrKwuHDx9GYGAgtm/fbnItZ2dnTJo0Sdzu3r077rnnHnG7arLcUAqFAqtWrcIDDzyA33//HUVFRXj00Ufxr3/9q85zn332WfH/dTodunXrZnT8ypUr+OGHHwAAzzzzDAYPHozvv/8eb775Jrp06SKW27hxI/bt24egoCCj88eMGSP+f3p6OrZt24Z9+/bBz88PCxcuxP333y8eX7JkCZYsWSJuP/744/j3v/9tErOrqyumTp0qbguCgCeeeAJXr17F7t27a032161bV+MxIiKiypjoExFRq3PnnXdi3LhxOHbsGNLT0/H333+blJk8eTI8PT2xdu3aaq8hl8uh0WiwfPly9OnTB/fddx8AYMOGDRg0aBCCgoLw2WefAShrMd+7d2+11wkODsZDDz2E/v374/Lly9BqtXBwcDAp99VXX+Hdd9/F1q1ba+xer9frIZPJxO3ly5cjPT1d3H7llVfE/58wYQL69u2Lb7/9FlqtFomJiVi+fLnJNYOCgowS/U6dOmH27Nni9sGDB1FUVIT4+HgkJiZWG1dycjIEQcC+ffsAAB4eHujbty8uXryI3NxcDBw4EKtXr4ajoyNcXFwAlD1gqIuDgwN0Oh2Ashb0N998Uzy2Zs0a+Pv7Y+LEieL7eOGFFzBo0CCjRPvWrVs4fPgwJkyYgA4dOhhdv3Ki/tprr6Fv37647777oFKpEBoaiv379yM7OxuTJk3CkiVLcPvttwMAHn74YRgMhjrjB8rmdjh06BCGDh2KlJSUasv4+/vX634QERGVY6JPRESt0qJFi6DX69GmTZtay1XX/brc6tWrkZKSgjVr1kAikeDEiRN45513MGXKFHh5eSErKwtr167F0KFDqz1/4sSJRt2/P/nkE/zwww+Ijo5G+/btxf2//vor/u///g+vvvoqOnfujIyMDHz22WeYNWsWvL29AQAXL17Ec889h5dffhkjR44Uzx02bBj+7//+z+h177rrLgCAWq3GF198gd69e+Pzzz83iW/q1KkoKSmp9f6U27VrFz7++ONayzz99NMAgKioKGzatAkbNmzAmTNncODAAURERKCwsBDbtm2r9Rp33323+DCgMolEYpTAf/nll2jfvr24b9GiRSgoKDAZ37569WoIgoDnnnvO5JqV7+P//d//oX379ggPD0dCQgJu3bqF48ePo23btgCArl27IjAwEEBZLwWtVlvr+wCAn376CR9++CGAsgcmBw8erLbc6tWrjWIhIiKqCxN9IiJqlTw8PMT/T0hIMEn0bty4AZlMZjJbvLe3Nz777DP88ccfWLduHZ577jmEhoZCrVZj2bJl8PX1xYMPPohp06bh9ttvrzHJBwCtVguFQiFuDx8+HFu3bsWkSZOwatUq9OvXD19++SXeeecdvPLKKxg6dCji4uIwb948ZGRkYNSoUfD29sZXX32Fd955B8HBwQgJCTF6DYVCYTKeXyKRAChLhjMyMrBs2TIolUqT+ARBqLZ3QXVmzZoltp5XFR0djd27d+Onn34CAJMhDuWysrKwaNGiWl8nKiqq2kS/LtOmTUNeXh6mTZtmtD8tLQ0ymUx8IBAdHY2uXbtCr9cjPj4ecXFxuHjxInJycrBhwwasW7cOXl5eWLhwIZYuXQqDwQAHBwd06tRJvKZCoRDH6586dQqdOnUy+rwBZT00lixZAplMhkGDBmH16tVGn4WTJ0/i6aefRt++fes9bwAREVE5JvpERNRqbNmyxajVdP78+QgLC4Obm5vRuPPysk5OTib7y5PM33//HXq9HqtXr8aHH34odtVeu3YtPvroIwDAG2+8AUEQIAgCpFLTFW01Gg1cXV3F7Y4dO2Lr1q2YOXMmtm/fDl9fXyxbtgyCIGDx4sViYunt7Y0vvvgCXbp0wcWLF7F8+XJMmDABr776arUJe3XKl7Xr379/jZPZ6XS6el/Pw8PDJJktV/4eK/dSqM2nn34qdoMvd/z4ccyaNavGc86fP2+0ZF5eXh6uX78uziEglUrx6KOPipP+VZWRkYGjR4+KPSyKioowfvx4CIKAtm3bQqfTYfDgwZgzZw66dOkCFxcXuLi44KmnngJQ1vvh1VdfRWRkJFxcXMRJC1evXg03NzesXLlSfK2YmBi89dZbuOeee/Dyyy/jiSeewOzZs/H222/D19cXn376KVatWoV77rkHS5curffDFiIionJM9ImIqNUonxk9Pz8ff//9tzhLvre3t5iwldu3bx88PT1N9pebPHkyIiMj4enpiYSEBLzwwguYOHEihg4disjISFy8eBG+vr6YMWMGIiMjMW/ePJNraLVak9ZtX19ffPXVV+IDhW+//Rb+/v7IysrCk08+CblcjvXr14vJX5cuXbBnzx6TieTqIpVK8e233+LatWuYN28eHn/8cZPJ7HQ6ndGDCAC4fv260Qz7GRkZ4uoD+/fvx7p16/Dpp5/WOYO8pR08eNBkpv5bt27h3LlzAMrG8F+4cAG9e/eu9vyTJ0/i6NGj4rabmxs++eQThIWFwc/PD8OHD0dYWBj69esHAPjzzz/x+uuvo02bNli0aBHef/99PPzww9i1axfatGkjLid448YNk6UVo6KisG7dOrGl/ssvv8TChQtx7733wsfHB1lZWXj77bcxduxYy9wcIiJqdZjoExFRqzFt2jRMmzYNJ0+exIwZMwCUddvPzs42KVtcXAy5XI4//vij2mt16dIFAwcOhFqtxosvvojg4GC8+uqrAMpasKOiogBAnNAvKirKpJVarVYbddcup9fr8cYbb+D69evYtGkTzpw5g2eeeQYdO3bEypUrsWfPHkRHR+O7777DpUuX8Oqrr2L+/PmYMGGC2C2/XGFhIeLi4oz2lfc+UCgU8PX1RWxsLJ566ils374dPj4+YjmNRmMS35kzZ3DmzBmjfeUPCAwGA/788098/vnnePLJJ6u9b/Vx4cKFeu2r7Nlnn8Wzzz4LnU4HuVyOjIwMDB48GO+88444UWJD9e3bF3K5HLm5ucjNzYVCoUBeXh4+/vhjfP755/Dx8cG6devQuXNnDBs2DAcOHEDnzp3RtWtX7N+/Hy+++CJu3ryJ8PBwk2vfcccdKCgoQExMDA4ePIhTp07B0dERHh4euHHjBlatWoVTp06he/fuCA0NRceOHRv1HoiIqHViok9ERK3aqlWrsGfPnhqPP/zww9Xu37VrF8LCwvDOO+/g+vXrRq3wlb355ps4c+YMXnrpJfzwww9G4+XVarVRi35paSm++OILrF27FgqFAvPmzcPnn3+Od999Fw8++CCeeuopFBUVoVu3btDpdJg/fz5WrVqFIUOG4JVXXsG3336LpUuXGs0ef+zYMYwbN67G9+fh4YEPPvgADz/8MJ599lls2rRJ7C1QXdf9+++/H8uWLRO3Z86cieLiYgBlk9d16dIF69evx7/+9a8au/LX5X//+1+jzissLERkZCSWLl1a7dwIV69exYQJE6o9VxAEk32rVq3Cxo0bAZT1gOjfvz+OHTuGjRs3YvTo0Xjrrbfg6ekJAHB0dBRb4O+//36sX78eP/74I9zc3Exi2bx5M7Zv3464uDg4OzvjjjvuwLJly3DnnXfCwcEBSUlJ+PXXX3Ho0CF8++230Gg0+M9//tOoe0JERK0TE30iImrVXnvtNbz44osm++fOnQt3d3eTNesPHDiAZcuWwdXVFd999x02b96M5557DjKZDHv27EFiYiIyMjLEpd68vLzwxhtv4JlnnsGiRYuwZs0a8VqlpaViov/333/jySefRFZWFqZMmYJ58+Zh165d4pJ3W7ZsEceby+VyuLi4iK3ny5cvx7hx47BgwQLcf//9+Oijj8TeA1FRUXj++eeN3sPjjz9utN2zZ08899xzWLFiBd577z28/PLLAKpv0ZdIJEYrEVTuQSCRSPDkk0/i+eefx4YNG6q9r/Xx3nvvITIy0mjf6dOnMX/+/FrPKygoAABxKAFQtrTf+fPnAQC33XYbli5dWu25CQkJJqsGTJkyBd26dYNUKkVYWBhCQ0MBlC2JGBcXh1u3bomJfmWBgYHYtm0bjhw5giFDhpg88OjevTuys7OxaNEi9OrVC88++yw2b96Mo0eP4rbbbkNoaChGjx6NWbNmQaPRIDY2Ft27d692ZQQiIqLqMNEnIqJWzcvLy2RWeqBsjXalUikumVauvOXX3d0db7zxBgBg5cqV4mRrDg4OJl3077rrLgwfPhx//vknbt68iXbt2kGv1xuN0e/UqRMGDBiAWbNmoWvXrgCA0aNHQyqVIigoCN7e3jh79izy8vIwd+5cAMC6devEFvfbb78du3btwrvvvosePXqIr+3p6Ym+ffsaxSOTyUze7+zZs7F3717s3r0bc+fOhZubG9Rqdb0n46v8XgMDA+vsal8bT09P+Pv7m+yrS15eHgCISw4Cxj+by5cv19iN/+TJkyaJvo+PD7p06SJux8fHAyh7QPPKK6+IcyZURyqVYvLkydX28ujVqxd69eolbg8ePBgJCQm4du0aDhw4gPT0dADA119/jV69eonDQIiIiOqLiT4REVEd0tPT4eTkBI1Gg3379sHf3x/Ozs645557IJVKERISgg4dOqBDhw4ICgqqNvl76623IJfLxYcK5d3dyyet02q1WLBgAYCyCe6AshbyyhO5xcTE4MSJE3jwwQcBlHURr1weAF544YVqu6HXRSaT4YMPPoCbmxvc3NwAmA4tqO91tmzZAl9f3wbHUC4nJwdpaWkm++qSlJQkxlDuxRdfrHWJw9qcOHECTz/9dI3HP/74Y5OHA1WPDxs2rMbje/fuhYODA/r3748HH3xQfKiSm5uLK1euoGfPno2Km4iIiIk+ERFRHR566CEkJyeL26+88goAiN3q66Nq4ltUVASgItH/5JNPak0aKxs8eHCtxydMmGAy5KA+Ks/cr9FooNPpGpzoA6bvtaHq6qJfk2vXrgEAPvjgA6xYsQIA4Ofnh86dOwMAxowZg8TExHpfb+TIkbh8+bLJ/tdffx27d+/GsWPHqr0/hw4dwpw5c+Du7l7jtfV6Pd59913cuHEDQNlDHT8/P3To0AEdO3ZEp06dIJVK0alTJ/HBCxERUX0x0SciolYnJiYGQNnkbfXx2muvISkpCXK5HN26dTPqGl9Op9Ph+vXriI+Px5UrV3DXXXeJCWZ1ytdZLx9P/sADD6B///61xrFhwwacPXsWq1atqrWcuYk2UNENvuoyeTt37sTOnTuN9lVdls9cL730kjh8oSo/P78azztz5gzCwsJw+fJlvP766ybH161bB41GU+2558+fx3/+8x+TVQuqOnHiBHbs2IEHH3ywxocg5as4VDckpJxMJsOvv/6KwsJCJCYmiv8uX76M/fv3Y9OmTQCAp59+Gs8991ytMREREVXFRJ+IiFqVVatWITo6Gp6enli6dCnatWtX7fJnlVXX9fvo0aM4cuQIrl+/jmvXriE5ORlarRZAWevsoEGDar1m+cOG8jkAgoODERwcXOs5u3btglwuN5kDoDZ79+5FWFhYvcuXK19WsOpY+YiICNx7773i9pdfflnntfLy8sSZ/Gvj4eGB559/HmPGjDGZG6EuGo0GMTExmD17Nnx9fcWlDsuHSADGPRaqCg0NFYdCVKewsBBffPEFPvroIwQGBuLf//53jWXj4+MhkUiMliqsiYuLCyIiIhAREWG0Pzc3FxcvXkSnTp3qvAYREVFVTPSJiKjVWLt2LaKjozFlyhQ8//zzmDZtGh544AEMHjwYERER8PT0hFwuh0wmw4wZMyCXy7Fr1y5otVrxn06ng1arhUajwebNmxESEoJu3brh/vvvF8fqBwUFia29JSUleOyxx+Dt7Q13d3eoVCpkZWVh7969cHV1tfpEa507dzZZXi86Otpo+5tvvkFMTAx8fHzg5OSEvLw87Ny5E3K5HAMGDDAq27FjR8ycOVPc/uOPP1BSUiJuFxQU4H//+x+cnZ2hVCqRk5ODvXv31tq7oZy7uzueeuqper+3rKwsceK9PXv2oLCwEIMHD0aPHj2g1+uxZMkSvPnmm/j+++/Ru3dvtG3bFo6OjpBKpWLLvcFgEP+V/2yDgoIwYsQIZGVl4fTp0zhw4IDY+n7nnXdi+fLlcHFxgUajwYIFC+Di4gJHR0c4ODggJycHP/74I3r06AFnZ2eTmBMTExv14IWIiKghmOgTEVGrERAQgGHDhuHNN9+EVCrFzp078cUXX+DXX3/F5s2bUVBQAJ1OV69rvf322/jzzz9Nlp+rytHREXl5eTh79qx4bZlMho4dO+I///lPrd27LaFDhw6YM2eO0b5PPvnEaFsmk+GHH34w2ufj44PXXntNXFIOALZv3260dB1g+tDA1dUVe/fuFYcmSCQSdOjQAa+99ppZ7yM1NRUfffQRnJ2d4eTkhKtXryI+Pl7sOXHz5k107txZHFYxZcoUDBo0SFzmbseOHWJMdXnzzTdx+fJlTJgwAXq9HgqFAkOHDsX06dONHswoFApcuHABN27cECdAdHFxQf/+/Wt8v97e3njmmWcadQ8++OCDRp1HREStj0RozNS8REREzZBer0dJSUm1S55VVt56r9frIQgCJBKJydhtJycnSKXSBr2+wWBAaWkpHBwc6tWVvaqFCxfit99+w8mTJ+tVPjk5GUqlsl5dyA0GA9RqNdRqNeRyeZ33qC56vR56vR5SqbTGJej+/e9/48yZMzhw4ECd11Or1ejXrx/UajWAsvs/YMAALFu2DF5eXtBoNDh16lStQybKW+zLYzMYDGKCXv5zBgA3NzfI5XJ8//33YuJe1/2ofH5NFi5ciLNnz+Lnn3+u8/1WZ/jw4Rg8eDAWL17cqPOJiKj1YKJPREREzYperzdaQo+IiIiMMdEnIiIiIiIiakEa1ueQiIiIiIiIiOwaE30iIiIiIiKiFoSJPhEREREREVELwkSfiIiIiIiIqAWpfr0bAgDk55dArzfYOgwiIiIiIiJq4WQyKdzcHC1yLSb6tdDrDdDpmOgTERERERFR88Gu+0REREREREQtCBN9IiIiIiIiohaEiT4RERERERFRC8JEn4iIiIiIiKgFYaJPRERERERE1IJw1n0iIqJWShAExKeqcSa+GEVqPZyVMvQJdUJogBISicTW4REREVEjSQRBEGwdhL3KySni8npERNQiJWdqsG5vBhLT1SbHOvgp8fhoHwR6K2wQGRERUeskl0vh6elskWux6z4REVErk5ypwdKvU6pN8gEgMV2NpV+nIDlT08SRERERkSUw0SciImpFBEHAur0ZKFbX3mOtWG3A+r0ZYMc/IiKi5oeJPhERUSsSn6qusSW/qoR0NRLS6leWiIiI7AcTfSIiolbkTHxxg8qfvtqw8kRERGR7TPSJiIhakZxCXYPKF6n1VoqEiIiIrIXL6xEREbUCgiDgxKVCxMQVNug8Z6XMShERERGRtTDRJyIiauFyi3T4bF9mg7vtA0BkRycrRERERETWxESfiIiohSprxS/Cpt8yUVRa+yz71QnxUyLEX2mFyIiIiMiamOgTERG1QLlFOny+LxOnq2nFD/CSI6dQj1JNzUvnOSmleGy0DyQSiTXDJCIiIitgok9ERNSCCIKA3y8X4YsDpq34Milw3wBP3NvPA2k5Wqzbm1HtUnsd/BR4fLQvAr0VTRU2ERERWZBEEISaH+e3cjk5RdDpGt7VkYiIyBZqa8Vv76vA46N9EOxT0RVfEAQkpKnx0Z5buJVXMRv//2YFws+TST4REVFTksul8PR0tsy1LHIVIiIispn6tuLLZcbd8CUSCUIDVBjczRU7jueI+xPSNUz0iYiImjEm+kRERM1YXpEOn+3PxOmrpq34wT4KzBlj3IpfndAqE+7Fp5ZiYLiLReMkIiKipsNEn4iIqBmqsxW/vyfujTJtxa9O1Zn149NMx+0TERFR8yG1dQDlBEFAdHQ0Bg8ejF69euHpp59GdnZ2jeULCwuxdOlSLFq0qNrjGo0Ga9aswWOPPWatkImIiGwir0iHVT+k46M9t0yS/GAfBd56uB3uH+hZryQfAJxVMgR4OojbN25poNNzCh8iIqLmym4S/fXr12PTpk1YvHgxNm7ciMTERLz88ssm5YqKivDhhx9i5MiR2Lp1K6rOJajX67Fu3TqMHj0aa9asgU6nM7kGERFRcyQIAn6/VIhXPk/GH1W66sukwISBnnjzoXZ1dtWvTkhAxTlavYAbGWzVJyIiaq7sItE3GAzYsGED5s6di+HDh6NPnz5YuHAhDh8+jKSkJKOyKSkpOHz4MBYvXozevXubXKu0tBQ7duzAU089hbFjxzbVWyAiIrKq8lb8NXtuobCGVvwJDWjFr8p0nD4TfSIioubKLsbox8XFIScnB0OGDBH3RUVFQSqVIjY2FkFBQeL+jh074ttvvwUAbNq0yeRazs7O+OmnnwAAp0+ftnLkRERE1iUIAk7GFeGL/ZkmCb5MCozv74lx9RyLX5vQAJXRdnyaGneZdUUiIiKyFbtI9Mtb7Ssn9CqVCl5eXkhLSzMqK5GY90WmoZr45YiIiER5xXp8vi8Tp64UmRwrn1G/vW/Du+lXJ9hHAQe5BFpd2ZC4hDQ160AiIqJmyi4S/aKiIkilUigUxmv2qlQqaDQaG0UFeHo62+y1iYio9RIEAYfP52LN9zeRX6w3OiaTAv8a5ocpQ33hILfsCLxO7Rxx4XrZ2P+0HC2UTo5wdbKLrwpERETUAHZReysUChgMBuh0OsjlFSGp1Wo4OjraLK6cnCLo9Ya6CxIREVlIba34QT4KzBntg9v8lMjLNT1urmBvB1y4XrF96u8s9OjgZPHXISIiIlMymdRijc12kej7+fkBANLS0hAYGAigbHm8nJwco+78tiBwdSEiImoiJy8X4osDmSgoMR2LPy7KA+P7l022Z626KcTPeBjA1VQ1Im5jok9ERNTc2EWi361bN6hUKhw/fhyTJ08GAMTExEAikaBv3742jo6IiMi68ov1+Hx/Da343go8PsYHt1loLH5tQgOqzrxfavXXJCIiIsuzi0RfpVJh6tSpWLlyJQICAuDk5IRly5ZhypQpcHNzw+zZszFlyhSMGjXK1qESERFZVExcIT7fb9qKL5WUteLfN6DxS+Y1lLebHK6OUjGWhDQ1BEFo8olwiYiIyDx2kegDwPz581FaWop58+ZBJpNh/PjxWLBgAXQ6HRISEnDr1i1bh0hERNRggiAgPlWNM/HFKFLr4ayUoU+oE3zc5fjiQFbNrfj/jMVvShKJBKEBKpxNKJuQr7DUgFu5Ovh5OjRpHERERGQeiSBwFHpNcnKKoNNxMj4iImqc5EwN1u3NQGK62uSYVAIYBNN9Td2KX9X3v+fg2+M54vaTd/vg9i6uNomFiIioNZHLW9hkfERERC1NcqYGS79OQbG6+gfGVZN8W7XiV2U6Tl/NRJ+IiKiZYaJPRERkYYIgYN3ejBqT/KrGRbljwkAvm7XiV9ahyoOG+DTT3ghERERk36S2DoCIiKiliU9VV9tdvyZ9Qp3tIskHAGeVDAFeFWPyb2SoodVxlB8REVFzwkSfiIjIws7EFzeo/OmrDStvbaH+Fa36On1Zsk9ERETNBxN9IiIiCytS661a3tpCA1RG2/GpTPSJiIiaEyb6REREFuaslFm1vLVVbtEHOE6fiIiouWGiT0REZGF9Qp0aVD6yY8PKW1ugtwIOleYMiE8ttWE0RERE1FBM9ImIiCwsNEBpMnt9TUL8lAjxt+2SelXJZRLc5qcQt2/l6VBQYl/DC4iIiKhmTPSJiIgsTCKR4PHRPnBS1l7NOimleGy0DyQS+5hxv7Kq4/QTOE6fiIio2WCiT0REZAWB3gosmtK2xpb9ED8lFk1pi0BvRbXHbc10nD677xMRETUXclsHQERE1FIFeivw5kNtkZCmxumrxShS6+GslCGyoxNC/JV22ZJfLjSgSqLPFn0iIqJmg4k+ERGRFUkkEoQGqEy6wtu7Nq5yuDvJkFdcNjY/IU0NQRDs+uEEERERlWHXfSIiIjIhkUgQUqlVv0htQFqu1oYRERERUX0x0SciIqJqVR2nzwn5iIiImgcm+kRERFStqsMNOE6fiIioeWCiT0RERNUK8VOi8oj8+DQm+kRERM0BE30iIiKqlqNSirZtHMTtGxlqaHQGG0ZERERE9cFEn4iIiGoUUmmcvt4AXL+lsWE0REREVB9M9ImIiKhGJuP02X2fiIjI7jHRJyIiohqZzrxfaqNIiIiIqL6Y6BMREVGNAr0VUMgrpuRjiz4REZH9Y6JPRERENZJJJejgV9Gqn5GnQ36x3oYRERERUV2Y6BMREVGtQqp0349PY/d9IiIie8ZEn4iIiGoVGlAl0U9l930iIiJ7xkSfiIiIalV15v0EjtMnIiKya0z0iYiIqFZeLjJ4OMvE7YQ0NQyCYMOIiIiIqDZM9ImIiKhWEonEaJm9YrUB6TlaG0ZEREREtWGiT0RERHUKqdJ9n8vsERER2S8m+kRERFSn0Koz73NCPiIiIrvFRJ+IiIjq1MFfCUmlbS6xR0REZL+Y6BMREVGdHBVStGvjIG4nZWig0RpsGBERERHVhIk+ERER1UvlZfb0BuB6hsaG0RAREVFNmOgTERFRvYSYjNNn930iIiJ7xESfiIiI6iU0oEqiz5n3iYiI7JLdJPqCICA6OhqDBw9Gr1698PTTTyM7O7vG8oWFhVi6dCkWLVpkciwuLg4PP/wwevTogREjRuC7776zYuREREStQ2AbBZQOFVPyceZ9IiIi+2Q3if769euxadMmLF68GBs3bkRiYiJefvllk3JFRUX48MMPMXLkSGzduhWCIBgdLywsxKOPPorAwEB88803eOCBB/DKK6/g7NmzTfROiIiIWiapVIIOfhWt+pn5OuQX620YEREREVXHLhJ9g8GADRs2YO7cuRg+fDj69OmDhQsX4vDhw0hKSjIqm5KSgsOHD2Px4sXo3bu3ybV27NgBvV6PJUuWIDw8HE899RQiIiKwZcuWpno7RERELVYox+kTERHZPbtI9OPi4pCTk4MhQ4aI+6KioiCVShEbG2tUtmPHjvj2228xatSoaq918uRJDBgwAAqFQtw3YMAAtugTERFZQEilmfcBjtMnIiKyR3JbBwBAbLUPCgoS96lUKnh5eSEtLc2orEQiQW2SkpJw5513Gu0LCAhAenp6o2Kr4+WIiIhalY5VJ+RLVbOuJCIisjN2kegXFRVBKpUatcIDZcm+RtOwNXqLi4uhUhm3Njg6Ojb4OgDg6enc4HOIiIhaMm9voI1bCrLydQCAxHQ1vLxcIJUy2yciIrIXdpHoKxQKGAwG6HQ6yOUVIanVajg6Ojb4Wlqt1mifWq02Sf7rIyenCHq9ocHnERERtWQdfBViol+sNuD8lWy0a6Oo4ywiIiKqjUwmtVhjs10k+n5+fgCAtLQ0BAYGAgA0Gg1ycnKMuvPX91pVu/unpqY2+DrlqkzqT0RE1OqF+Kvwx9VicTs+VY22Xkz0iYiI7IVdTMbXrVs3qFQqHD9+XNwXExMDiUSCvn37NuhakZGR+P3336HXVyz38/vvv2PgwIEWi5eIiKg1CzUZp8+Z94mIiOyJXST6KpUKU6dOxcqVK3HkyBGcPn0ay5Ytw5QpU+Dm5obZs2fjl19+qde1Jk2ahPz8fCxevBhxcXFYvXo1Ll++jOnTp1v5XRAREbUOHfyURhPwceZ9IiIi+2IXXfcBYP78+SgtLcW8efMgk8kwfvx4LFiwADqdDgkJCbh161a9ruPn54ePP/4YS5YswbfffotOnTph3bp1aNeunZXfARERUeugUkgR2EaBpMyyiW6TMjRQaw1QOthF+wEREVGrJxEEjkKvSU5OEXQ6TsZHRERU1YZfMnDorwJx+9UpbRHWruET3xIREVEZudxyk/Hx0TsRERE1WNVx+gkcp09ERGQ3mOgTERFRg4X6G7fec5w+ERGR/bCbMfpERETUfLRr4wCVgwSl2rIRgPGpTPRtTRAExKeqcSa+GEVqPZyVMvQJdUJogBKSyrMnEhFRi8dEn4iIiBpMKpWgg58SF5PLuuxnFeiQW6SDhzO/WthCcqYG6/ZmIDHd+IHLj6dy0cFPicdH+yDQW2Gj6IiIqKmx6z4RERE1SkiVcfps1beN5EwNln6dYpLkl0tMV2Pp1ylI/meVBCIiavmY6BMREVGjVB2nn8Bx+k1OEASs25uBYnXtqwQVqw1YvzcDXGyJiKh1YKJPREREjVJ15v14zrzf5OJT1TW25FeVkK7mwxgiolaCiT4RERE1iqeLHF4uMnE7IV0Ng4Etxk3pTHxxg8qfvtqw8kRE1Dwx0SciIqJGCw2o6L5fqhGQkq21YTStT5Fa36DyNzLUMLD7PhFRi8dEn4iIiBotxL9K9/00dt9vSs5KWd2FKjl3rQSvfJ6M387lQ6OtfVw/ERE1X0z0iYiIqNGqjtNP4Mz7TapPqFODz0nN1uLTfZmYt+4Gth/LRm6RzgqRERGRLTHRJyIiokbr4KeEVFKxHc/J3ppUaIASHfyUdResRmGpAbtO5mL++htYt/cWbmTwZ0dE1FIw0SciIqJGUzpIEeitELeTMjVQs0t4k5FIJHj0Lu86yzkppXhwkCeCKv2syun0wJG/C7Fo0038d3sqYhOLOY6fiKiZk9s6ACIiImreQv2VuJGhAQAIApCYrkZ4oKONo2o9UnNqnwAxxE+Jx0b7INBbgXFRHriYVIqfTuchNtF0Bv6/b5Tg7xslaOvlgNF93DGoiwsUDmwXIiJqbpjoExERkVlCApT47XyBuB2fykS/KR2IzTfa7tvRCa5OMjgrZYjs6IQQfyUkkrLxFRKJBF2DHdE12BEp2RrsPZ2HoxcKodUbt+Cn/DOOf/uxbIzo6YYRPd3g7syvjUREzQX/YhMREZFZQv1VRtsJHKffZJIzNbiUXLHSgZeLDE/f6wdZ5YkTatDWS4FZd/nggUFeOHAuH/vO5iO/2Hi5voISA777PRc/nsrF7eGuGBPpbjRUg4iI7BMTfSIiIjJLWy8HqBQSlGrKWoXjU7nEXlM5cM64NX9YD7d6JfmVuTnJcP8AT9zT1x2/XyrCz6dzkZxlPBxApwcO/12Aw38XIKK9I8ZEuqN7e0expwAREdkXJvpERERkFqlUghA/JS4klSX42YV65BTq4OnCrxnWVKox4OiFiiETMilwZ4Rro6+nkEtxR3dXDOnmgr9vlODn03k4d63EpNz56yU4f70E7do4YEykOwaGu0Ah5zh+IiJ7whqYiIiIzBYaoBITfaBsnH7fTvyaYU0nLhWKvSgAILKjMzwsMI5eIpGge3sndG/vhJtZGuw9k4dj1Yzjv5mlxYZfMrHtaI44jt/NSWb26xMRkflYAxMREZHZQvyN13KPTytF307ONoqm5RMEAfurTMI3oqebxV+nXRsFHr3LBw8O8sL+2HzsO5uHghLj5RPzi/XYeSIHP8bk4vYuLhgT6Y52bYzH8QuCgPhUNc7EF6NIrYezUoY+oU4IDVCy+z8RkRUw0SciIiKzhQYYJ/oJqZyQz5qupqrFJQ2BsnkSwgNVtZxhHjcnGSYM9MTYfu44cbEQP5/Jw80q4/i1egGH/irAob8K0OO2snH83YIdcTNLi3V7M5CYbvyZ+PFULjr4KfH4P0v/ERGR5TDRJyIiIrN5OMvRxlWOrAIdACAxXQ2DQYC0gRPDUf1U15rfFC3jCrkUd0a44Y7urjh/vWwc/1/XTcfxn7tWgnPXSuDvIUd2oR4anVDN1co+J0u/TsGiKW2Z7BMRWRATfSIiIrKI0AClmOiXagXczNIiyIfJm6UVlOgRE1cobivkEgzq2vhJ+BpDIpGgx21O6HGbE5IzNfj5dB6OXyqAznh1PqTl6uq8VrHagPV7M/DGQ21t0o2fwwqIqCViok9EREQWEeqvRExckbgdn1bKRN8KDv9lnFAP6uICJ6XtZr0P9FbgsdE+mDTYE/tj87E/Nt9kHH9dEtLV+Pl0Hjq2VcFZJYWzUgpnlQxymXUT7eRMDYcVEFGLxESfiIiILCIkwHiMeHyqGkMjbBRMC2UQBBw4Z9xtf7gVJuFrDHdnOSbe7oV7+3ng2MVCbDuajcLS+if8Ww5nm+xTOkj+SfxlcFFJ4aSSwkUlg5NSChfHsv3OKuk//2T/PCCQwlEphbSO1vjkTA2Wfp2CYnX1MXJYARE1Z0z0iYiIyCJu81VAKgEM/wzHTkjjhHyWdv5aCTLyKrrDdwxQor2vspYzmp7CQYphPdyQmK7GwfMFZl1LrRWg1uqRXaCvu3AlEgngpCzrGeCikv3zgEAKJ9U/DwwUEuyLLagxyS9ny2EFHFJAROZgok9EREQWoXSQIshHgeu3ymaDT87SoFRjgEphu27lLc2BJlhSz1JcVDKbvbYgAEWlBhSVGnArr+55AmqTkK7G5weyEOKnhLuTDO7OMrg5lf2z1tACDikgInMx0SciIiKLCfVXiom+IJR1f+4S5GjjqFqGzHwtziYUi9suKin6dXa2YUS16xPqhB9P5da7/PCebnB0kKCw1IBitQGFpXoxWS9S61GqqX7m/qZwIDYfB6rZ76KSwt1Z9s8DADncnGTw+OdBQPlDAXcnGVydZJDVcwUKDikgIktgok9EREQWExqgwoFzFd21E9KY6FvKb+cKUDnVvaO7KxRy++0tERqgRAc/pUmrdHVC/JR4ZHibWruk6/QCitUGFFV5AFBYakBxlf8vLNX/87CgrHzV1QAspbC07DVuZmlrLScB4OIohcc/DwPKHwAYPRhwlsHNUYp1e2/Z9ZACImoemOgTERGRxYT4G48Xj08ttVEkLYtOL+DQXxUPUCQAhvWw3277QNkSfI+P9qm1dRooG0v/2GifOhNWuUwidplvCEEQoNEJRg8HfjmThz+uFtd9soUIAApKDCgo0VjsmgnpaiSkqRFaZRJMIiKAiT4RERFZUICXA5yUUjGxi+eEfBbxx5Ui5BdXNEtH3OYIPw8HG0ZUP4HeCiya0rba8eZAWUv+Y1Yeby6RSKB0kEDpIIWXa9k+uVTSoET/X3d4wUkpRX6xHnlFeuRV+m9+sb7OFnhrOX21mIk+EVWLiT4RERFZjFQiQQc/Jf6+UQIAyCnUI7tABy9XfuUwx/5mNAlfVYHeCrz5UFskpKlx+mrFDPKRHZ0Q4m+bGeQbOqzg7kj3WuPU6AzVPwQo3y7WI69Ih7xiy841UKS20pgEImr2WOsSERGRRYUGVCT6QFmrPhP9xkvO1ODyzYohEG1c5ejZwcmGETWcRCJBaIDKblqfLT2sQCGXwttNCm+3untZqLVVHgpU+v8L14uRllv/VQKclbZb2YCI7BtrXSIiIrKo0Crj9BNSS9Gvk/3ODm/vqrbmD+vhCmk9Z3CnmtlqWIHSQQofdyl83E0fClxNKcXirSn1vlZkx+b1wIeImo7dTNUqCAKio6MxePBg9OrVC08//TSys7OrLXvq1ClMnDgRERERGDt2LI4cOWJ0fN++fbj33nvRvXt3jB07FkePHm2Kt0BERESASastx+k3XonGgGMXKybhk0mBO7u72jCilqV8WMEbU9vi3n4eGNbDFff288AbU9vijYeafvm68iEF9RHipzSZ/JKIqJzdJPrr16/Hpk2bsHjxYmzcuBGJiYl4+eWXTcolJSVhzpw5GDhwILZv345+/frhmWeeQXJyMgDg/PnzeO655zB27Fhs3boVkZGReOqpp3Dt2rUmfkdEREStk5uTDN5uFZ0GE9PVMBhstwZ6c3biYqHRmO5+nZzh7swOmZZUPqxg8hAvzBrpg8lDvBAaoLLJ3AHlQwqclLV/RVfIJfUaUkBErZddJPoGgwEbNmzA3LlzMXz4cPTp0wcLFy7E4cOHkZSUZFR28+bNCA4OxksvvYSwsDAsWrQI7u7u2LFjB4CyBwaDBw/G3Llz0b17d7z11lto27YttmzZYou3RkRE1CqFBlS0NKq1ApKzLLesWGshCIJJt/3hzWgSPmqc8iEFtbXsuznJ0NbL/lddICLbsYtEPy4uDjk5ORgyZIi4LyoqClKpFLGxsUZlT548aVROLpejX79+Yrn4+Hh06dJFPC6RSNCvXz+cO3fOyu+CiIiIylXtUhyfyu77DXUlRY2kzIoHJO3aOCCsnX1MZkfWVXVIwdAIV7ioKr62Z+brcOpKkQ0jJCJ7Zxd9v8pb7YOCgsR9KpUKXl5eSEtLMykbGBhotC8gIACXLl0CAHh4eCAlxXgSk8LCwhrH+9eFPaKIiIgarmOVcfoJaWoM72mjYJqpA+dMl9TjJHyth0QiQce2KnRsW/a71DXIEWv23BKP7zqZi6gwZ0j5ZZWIqmEXiX5RURGkUikUCuMJT1QqFTQa465+xcXFUKlUNZYbPXo03nnnHdx77724/fbbcfDgQezbt8/k4UB9eHpyhmAiIqLGcHV3hkyaAv0/K5ddz9DC25uTyNVXbqEOMXEVLbYqhRTjhwTAWcXl1Fqrewa54PuTubj5zzCYpEwNEjIEDOjK4RxEZMouEn2FQgGDwQCdTge5vCIktVoNR0dHk7JardZon0ajEZP/hx56CNeuXcPcuXNhMBgQFhaGESNGIDMzs8Fx5eQUQa+veW1VIiIiqlmQtwLXbpUlJdfTS5GUkgdHhV2MGrR7P8bkQqevmITv9nBnlBQWo6TQhkGRzd3T1x3r9maI25t+TUWoj4ST8hG1EDKZ1GKNzRZJ9AVBQGpqKtLS0tC5c2e4uLg06Hw/Pz8AQFpamtjyrtFokJOTY9Sdv7xs1e78qampYjmZTIbXXnsNL730EnJychAQEIA5c+YYjdtv2Htr1GlEREStXkiASkz0BQAJqWp0DXas/SSCwVD9JHz8TkIDw12w43gOsgp0AMqGxJy/VoKI25xsHBkR2RuzH6t/9dVXGDJkCEaMGIGHH34YcXFxAID//ve/4v/XpVu3blCpVDh+/Li4LyYmBhKJBH379jUqGxkZiWPHjonber0eMTExGDhwoFE5lUqFgIAAJCUl4ejRoxg7dmxj3yIRERE1QmjVCfnSOCFffZy/VoLMfJ243amtEsE+XC+dALlMgnujPIz27TqZa5NYiMi+mZXof/HFF1i8eDF69OiB+fPnQ6j0qLmoqAhr166t13VUKhWmTp2KlStX4siRIzh9+jSWLVuGKVOmwM3NDbNnz8Yvv/wCAJg+fTrOnz+P6OhoxMXFYcmSJTAYDJgwYQKAsln39+3bhytXruDAgQOYM2cOxo0bh169epnzVomIiKiBKi+xBwAJqaU2iqR52RdrOgkfUbkh3Vzg4VwxV8Plm6W4lFxiw4iIyB6Z1XX/yy+/xOTJk7F48WIUFxdjxYoV4rFevXph9erV9b7W/PnzUVpainnz5kEmk2H8+PFYsGABdDodEhIScOtW2SyjXbt2xYoVK7BixQqsXbsWPXr0wMaNG8XhAiUlJXj77beRlpYGX19fTJw4EU888YQ5b5OIiIgawd/TAU5KKYrVZfPdxKepIQgCxxPXIiNPi3OJxeK2q6MU/To1bEgktWwKuRT39HXHV4cqVpTadTIX4YEcFkNEFcxK9FNSUjB48OBqj7m6uiIjI6PaY9VRKBR488038eabb5oc++2334y2x4wZgzFjxlR7ne7du2Pfvn31fl0iIiKyDqlEghB/Jf66XtbamFukR3ahHm1c7WIuYLv027kCVB6Kf0d3VzjI+WCEjA3r4YYfYnJRUFL2EO2v6yWITy1FaJVlLYmo9TKr676fnx/OnTtX7bFLly7BzY1dzYiIiFqzkKrj9Nl9v0ZanYBDf1V025cAGN6D36XIlNJBijGR7kb7OFafiCozK9G///778dlnn2Hz5s3iOvYSiQSnT5/Gpk2bMGzYMIsESURERM1T1XH68amckK8mp64UiS20ANCjgyN83B1sGBHZs5E93eGkrPgq/2dCMa7f4u8XEZUxK9F/8sknMWTIECxduhRDhw4FAMyePRvTpk1DmzZt8MILL1giRiIiImqmQv2NuxIncOb9Gu2PzTPaHs5J+KgWjkopRvU2/oz8EJNrm2CIyO6YNUhOLpfjo48+ws8//4wDBw4gMzMTrq6uiIqKwsSJE+HoyElBiIiIWjM3Jxl83OXIyCtbLi4xXQ29QYBMynHnld3IUONKSsVDEG83OXpybXSqw1293fHz6TyUastmdjgVV4SbWRq0a6OwcWREZGsWmQ2ntsnxiIiIqHUL9VeKib5GJyA5U4P2vlwXvrIDVZbUG9bDFVI+DKE6uDrKMKKnG3b/UdYbRADwY0wunrjb17aBEZHNmdV1v0uXLjh69Gi1x3bs2IGRI0eac3kiIiJqAUKqzAQez+77RkrUBhy/WChuy6TAnd3ZbZ/qZ0ykOxxkFQ+FTlwqRHqu1oYREZE9MCvRr20t3JKSEmRmZppzeSIiImoBQjnzfq2OXSwQu14DQL9OznBzktkwImpO3J3lGNbDVdw2CMDuU7m2C4iI7EKDu+7v2rUL27dvF7ffeecduLsbL+9RUlKCixcvIiIiwvwIiYiIqFlr76uATAro/5lQnhPyVRAEwaTb/ohe7jWUJqrePX09sD82X/wdO/J3Ae4b4Ik2rhYZpUtEzVCDf/s7dOiA5ORkAGVL6WVmZqKgoMCojEKhwPDhw/Hiiy9aJkoiIiJqthRyKYJ9lEhML0vwU7K0KFEb4Kg0q2NhixB3sxTJWRXdrAPbOKBzW85fQA3j5SrHHd1c8dv5su/kegOw51Qupg/3tnFkRGQrDU70IyIicODAAQBAeHg4/vvf/2Lw4MEWD4yIiIhajlD/ikRfAJCQrka3YK7Os7+a1vyahkUS1WZsPw8c+qsAhn9GgRw8X4Bx/T3g4cxWfaLWyKxH6cuXL0fnzp0tFQsRERG1UKEBHKdfVV6RDqeuFInbKgcJbu/iYsOIqDnz9XDAwEqfH61ewM+n82wYERHZklmJ/oQJE+Dry+U7iIiIqHYhVRN9jtPH4b8LxDHVAHB7V1c4KjicgRpvXJQHKvcH2R+bj4ISvc3iISLbMasvT0lJCd5//33s27cP6enpMBgMRsclEgkuXLhgVoBERETU/Pl7OMBZKUWRuuy7QkKqutbVe1o6g0HAgVjjOY5G9OCSemSetl4K9OvsjJi4sp4iaq2AX87k4YFBXjaOjIiamlmPjd9991188cUX8PPzQ9u2bREWFoZRo0ahTZs2CA4Oxn333WepOImIiKgZk0gkCKm0zF5esR5ZBTobRmRbsdeKjd5/53YqBPkobBgRtRTj+3sYbf96Nh/FakP1hYmoxTIr0d+/fz9mz56NLVu24J577kHPnj3x4Ycf4qeffoKLiwuGDBliqTiJiIiomTMdp996u+/vP1tlEr6ebM0nywj2UaJ3qJO4Xaw2YN9ZjtUnam3MSvTz8vIQGRkJAAgODhaX3XN1dcX06dOxcuVK8yMkIiKiFiHUX2W03VrH6d/K1eL8tRJx29VRir4dnW0YEbU046M8jLZ/Pp0HtZat+kStiVmJfkBAANLS0gAAoaGhuHDhgjhOXxAE8RgRERGRyYR8rXTm/d/O5UOotD00wg0O8tY5VwFZR2iACt3bVyxfWVhqwIFz+bWcQUQtjVmJ/tChQ/Hdd98BACIiIiAIAubPn4/Nmzdj5cqVCAkJsUSMRERE1AK4Osrg614xD/C1dA10eqGWM1oejc6Aw39XTMInATA0wtV2AVGLdV+Vsfp7/siDRsdWfaLWwqxE/7HHHsO0adNQUFAAmUyGxYsX49ChQ1i6dCny8/OxYMECS8VJRERELUBoQEX3fa1eQHKmxobRNL1TcUUoKKlItnqGOMHH3cGGEVFLFRboiLB2Fb9veUV6HP6roJYziKglMSvR9/Lywvjx4+HqWvYketSoUTh06BC2b9+OQ4cOYcCAARYJkoiIiFqGUP8q3fdb2Tj9qt2nuaQeWdN9AzyMtnefymt1vWiIWqtGJ/qCIKB///7YtWuX0X43Nzd0794dLi4uZgdHRERELYvpzPutZ5z+jQw1rqRUPNjwcZcj4jbHWs4gMk+3YEejZS2zCnQ4dpGt+kStQaMTfYlEAicnJ+j1ekvGQ0RERC1YsI8SclnFdmtq0d8fa9yaPyzCDVIpJ+Ej65FIJCZj9X+MyYXewFZ9opbOrK77r7zyCtatW4eLFy9aKh4iIiJqwRzkEgT7VLQwpmZrUVTa8hsNStQGHL9YKG7LZcAd3TkJH1lfrxAnBPsoxO30XB1OXi6yYURE1BTkdRep2ebNmyEIAiZOnIigoCD4+flBIql4Mi2RSPD555+bHSQRERG1HKH+SiRUaslPTFeje3snG0ZkfccuFkCtrWhFjerkAjcnWS1nEFmGRCLB+P4eiP7xlrjvh5gcDAh3hlTCHiVELZVZiX5ycjIAICAgADqdDjdv3rRIUERERNRyhQYo8evZiu341Jad6AuCYNJtf0QvTsJHTadvR2cEeDkgNVsLALiZpcXpq8Xo18nZxpERkbWYlegfOHDAUnEQERFRKxHqrzLabunj9C/fLMXNLK24HeStQMcqkxISWZNUKsH4KA+s/TlD3LfrZA76dnQy6o1LRC2HWWP0G0KtVmPGjBm4cuVKU70kERER2SFfDzlcVBVfQeJTSyEILXdysOpa85lcUVMbEO4CH/eKNr7rtzQ4l1hiw4iIyJqaLNHX6/WIiYlBQQGX9CAiImrNJBKJ0ZJfBSUGZObrbBiR9eQW6fDHlYqJz1QKCW4P5xLE1PRkUgnu7edhtO+7kzkt+iEbUWvWZIk+ERERUbnQgCrd91NbZvf9w38VQG+o2B7cxRUqBb9+kW0M7uoKL5eKSSDjU9W4mFRqw4iIyFpY0xAREVGTC/U3HqMen9bykg2DQcCBc8bd9of35CR8ZDsOcgnuqdKq//3JHNsEQ0RWxUSfiIiImlxI1US/Bbbon00sRnaBXtwOa6dCoLeiljOIrG9od1ejpR0vJpUi7mbLe9BG1Nox0SciIqIm5+Iog5+Hg7h9/ZYGOn3LGit8oOokfGzNJzugcJDi7kh3o3272KpP1OIw0SciIiKbCK20xJxWLyApU2PDaCwrPVeLc9cqZjR3c5KhL9csJzsxvKcbnCutfHHuWgkS01terxqi1qzJEn2ZTIZ+/frB1dW1qV6SiIiI7JjJOP3UltN9+LcqY/Pv7O4KuYxL6pF9cFRIMboPW/WJWrImS/SVSiU2bdqETp06VXtcEARER0dj8ODB6NWrF55++mlkZ2dXW/bUqVOYOHEiIiIiMHbsWBw5csTo+KFDh3DfffchIiICo0ePxo4dOyz+foiIiMg8lVv0ASA+rWW0KGp0Bhz+q2I5YYkEGNaDDR1kX+7q5QZHRcXDp9NXi5HcgnrVELV28oYUHjFiRIMuLpFIsG/fvnqVXb9+PTZt2oTly5fDw8MDixYtwssvv4x169YZlUtKSsKcOXPw0EMPYfny5diyZQueeeYZ7N69G4GBgbh27RqeeuopPPbYY/jvf/+L33//Ha+++iratm2LAQMGNCh+IiIisp4gbyXkMkD3z3x1CS1kQr5TcUUoLK1YU69XByd4uznUcgZR03NWyTCylzt+iMkV9+06mYOnxvrZLigispgGtei3bdvW5F9eXh4yMjLg6emJtm3bQi6X4+bNm5DJZAgKCqrXdQ0GAzZs2IC5c+di+PDh6NOnDxYuXIjDhw8jKSnJqOzmzZsRHByMl156CWFhYVi0aBHc3d3FVvuLFy/CxcUFL7zwAsLDwzFz5kyEh4fjr7/+ashbJSIiIitzkEvQ3qeiVT81R4uiUn0tZzQP+zgJHzUTo/u4QyGvaNU/GVeE1By26hO1BA1q0d+0aZPR9rZt23Dz5k18/vnnRkn9H3/8gRdffBHz5s2r13Xj4uKQk5ODIUOGiPuioqIglUoRGxtrdO2TJ08alZPL5ejXrx9iY2MBAP3794dMJsMPP/yAe+65BydOnMDNmzcxbNiwhrxVkYTD6YiIiKwmNEBp1GU/MV2NiNucbBiRea6lq42WCvR1lyOigyO/T5BdcneWYURPN/x0Og8AIAjA7phcPD7G18aREZG5GpToV7Vu3To89thjJi33ffv2xcMPP4x33nkHX375ZZ3XKW+1r3wdlUoFLy8vpKWlmZQNDAw02hcQEIBLly4BALy8vLB8+XI8+eSTeOmllyAIApYuXYrQ0NAGvz9PT86OS0REZE29Ouvwy58VLeCpecAw7+Y7nv3Lw7lG2+Nu94GvD1v0yX49PEqFfbH50OrKlrc8drEQj44Ngp+nwsaREZE5zEr0U1NT4e7uXu2xoKAg/P333/W6TlFREaRSKRQK4z8oKpUKGo1x96Hi4mKoVKoay125cgUvvPAC5syZgxEjRuDkyZNYunQpOnTogL59+9b3rQEAcnKKoNcb6i5IREREjeLrIhhtn0/IR2bP5tmiX6w24MCfFTOXO8gkiLxNgczMglrOIrK9O7q5Yv8/Q070BmDT3puYOdLbxlERtT4ymdRijc1mJfrBwcH46aefMHbsWJNjhw8fhqOjY72uo1AoYDAYoNPpIJdXhKRWq02uoVAooNVqjfZpNBox+V+9ejX69u2LF154AQDQo0cPXLt2DStWrMCWLVsa9P6Asi5MREREZB0+bnK4OkpRUFL2YD0+tRQGgwBJM+zrfuTvAmh0FV8cojo7w8VRxu8SZPfG9nPHwfP5KG/fOvRXPsb394Cni1mpAhHZkFnL6z388MPYt28fnnnmGRw7dgyJiYn4448/8Nprr+G7777D6NGj63UdP7+y2T0rd9PXaDTIyckxGRbg5+dn0p0/NTVVLHf58mV06dLF6HjXrl1x+fLlBr8/IiIisi6JRIIQ/4oJ+QpKDMjI09kwosYRBAEHqkzCN5yT8FEz4e3mgEFdKobM6PTAnj/ybBgREZnLrET/oYcewtNPP42DBw/isccewz333IPp06dj27ZtGDlyJBYsWFCv63Tr1g0qlQrHjx8X98XExEAikZh0t4+MjMSxY8fEbb1ej5iYGAwcOBBA2YOAq1evGp0TFxcnPkwgIiIi+xLqbzwkr/LkfM3FpeRSpGRX9DgM9lGgY4CyljOI7Mu9UR5Gk0b+di4f+cXNfxUMotbK7P44zz77LKZMmYKjR48iIyMDXl5e6N69u0mrem1UKhWmTp2KlStXIiAgAE5OTli2bBmmTJkCNzc3zJ49G1OmTMGoUaMwffp0TJo0CdHR0Rg1ahS++uorGAwGTJgwAQAwbdo0PPPMM1i7di3uuOMOnDp1Ct9++y1eeeUVc98qERERWUFIlYQ4PrUUA8NdbBRN4+yvZkm95jj8gFovf08HDAhzwYlLhQAAjU7A3jN5mDTYy8aREVFjWGTgja+vLyZOnGjWNebPn4/S0lLMmzcPMpkM48ePx4IFC6DT6ZCQkIBbt24BKOuGv2LFCqxYsQJr165Fjx49sHHjRri4lH0hGDlyJN555x2sXbsWq1atQkBAABYsWICHH37Y7PdJRERElhfqXyXRb2Yt+rmFOpy+WiRuOyokze5BBREAjIvyEBN9APj1bB7u6esOZ5XMhlERUWNIBMG8KWJKS0tx7tw5pKWlwWAwnaH+/vvvN+fyNpWTUwSdjrPuExERWduCT5OQllPW9d1BJsHaZ26DXNY8WsS/+z0HO45XzLY/spcbZgznjOXUPK3clYY/rhaL2xMHeuL+gZ42jIio9ZDL7WTW/QsXLuDJJ59ERkaGuE8QjGfKbc6JPhERETWNUH+lmOhr9QJuZKgRUmXsvj3SGwQcPGfabZ+ouRrf39Mo0d97Jg+jI93hqDBrai8iamJm/ca+/fbbEAQBixcvxp133omxY8di7dq1mDx5Mjp06IC1a9daKk4iIiJqwUJNxuk3j+77ZxOKkV1YMWFZl0AV2rVR2DAiIvPc5qdEzw4Vy1sXqQ0mK0oQkf0zK9G/ePEinn/+eUyaNAl9+vSBRCLBnXfeibfeegt9+/bFwYMHLRQmERERtWRVW++byzj9qpPwcUk9agnG9zfuqv/T6TyotRzOStScmJXoy2QyeHh4AACCg4Nx7do18djgwYOxe/ducy5PRERErUSwjwIOlcbkN4cW/bQcLf66XiJuuzvLENnRMmMriWypU1sVugRVPHzLL9bj0PkCG0ZERA1lVqIfGhqK8+fPAwC6deuGS5cu4ebNmwCAv/76y/zoiIiIqFWQyyRo71vR5T09V4vCEvtbw1sQBFxNKcU3R7Kx6od0o2N3dndtNhMIEtXlviqt+rv/yIVWZ9Yc3kTUhMyajO/BBx/EihUrMHv2bAQFBSEiIgITJkxAYGAgLl68iHvuucdScRIREVELFxqgxNVKLfkJaWr06OBkw4iMJWdqsG5vBhLTq+9tEB5o/5MHEtVXlyAVOlb6ncwp1OPohQIM68HhKUTNgVkt+hMmTMDRo0fh5lb2C//+++9jyJAh0Gq1uO+++7Bo0SKLBElEREQtX6gdj9NPztRg6dcpNSb5ABD94y0kZ2qaMCoi65FIJCat+j/E5EKnZ6s+UXNgVos+AJSUlGDr1q24cOECsrOz4evri6eeegpjxowxWmaPiIiIqDamM++X2igSY4IgYN3eDBSra5+MrFhtwPq9GXjjobb8DkQtQo8Ojmjvq8D1W2UPsDLzdfj9ciEGd3W1cWREVBezWvTj4+MxatQo/Pe//8XevXtx8eJF7Ny5E/Pnz8eMGTNQXFxc90WIiIiIAHi7yeHqWPHVJCFNDUGwfethfKq61pb8yhLS1Uiwo54IROaQSCQmM/D/cDIXBoPtfy+JqHZmJfrLly9HcXExoqOjce7cORw/fhynT5/GCy+8gNOnT+P999+3VJxERETUwkkkEoQGVHTfLyw14FauzmbxaHQGXLhRgq8OZTXovNNX2dBBLUdkRye0a+MgbqfmaHHqSpENIyKi+jCr6/7p06fx1FNPYeTIkeI+Z2dnzJkzB9nZ2fjxxx/x6quvmh0kERERtQ6h/kqcTahIlOPTSuHn6VDLGZZjMAi4nqHB39dL8PeNEsTdLIW2EeORi9T2t1oAUWNJJRKMi/LExz/dEvftOpmLfp2dIeUQFSK7ZVair1AoEBwcXO2x3r17Y8uWLeZcnoiIiFoZ03H6atzexTrjgQVBwK08Hf6+UYK/r5fgQlIJikprH4dfH85KmQWiI7If/cOcsfOEHOn/9LBJytTgbEIx+oQ62zgyIqqJWYn+8OHDceLECYwePdrk2KVLl9C9e3dzLk9EREStTAe/Kom+hce75xfrceFGWYv93zdKkJlv+aEBkR3tZ0lAIkuQSSW4N8oDG37JFPft+j0XvUOcOPEkkZ0yK9FfuHAhnnjiCaxduxYPPPAAvL29UVJSgr179+KHH37ARx99ZKk4iYiIqBVwVskQ4OWA1GwtAOBGhhpanQAHeeOSCbXWgMvJpWJifyOj/svfebnI0K29I7oGqfDT6fx6nRvip0SIv7LOckTNzaAurvjuRC6yCsoejiWkq7Fm9y04qaRwVsrQJ9QJoQFKJv5EdkIiNGA62/DwcJNfXkEQqv2FFgQBUqkUFy5cMD9KG8nJKYJOZ34XPiIiIqq/T36+haMXCsXtyI5OCPBU1CuR0BsEJKSp8feNEly4UYIrKaXQ17Mqd1JK0SVIhW7BjugW7Ah/TwfxtZIzNVj6dUqtS+w5KaVYNKUtAr0V9XtBomZm39k8fHGg5skpO/gp8fhoH/4OEDWSXC6Fp6dlhsQ0qEX//vvv51M6IiIisiovV+OvJ2Wz2Bfjx1O5JomEIAhIydaKLfaXkkpQoqlfG4ZcBnRqW5HY3+anhExa/fecQG8FFk1pi3V7M6pdai/ET4nHmOBQC1d1Do2qEtPVWPp1Ch94EdmBBrXotzZs0SciImpayZkaLN56E6W1JOuOCgnG9vNAao4WF66XIKeofrPcSwC091WgW7AjugY7onM7FZQODVtpWBDKegycvlqMIrUezkoZIjs6IcSfXZapZRMEAW9+lVLtg66qQvyUeOOhtvydIGogm7XoExEREVmLIAhYtzej1iQfAEo0ArYfy6nXNX3d5WUt9u0d0SXIEa6O5s2IL5FIEBqgQmiAyqzrEDU38anqeiX5QNn4/YQ0NX9PiGyIiT4RERHZhYYkEjVxdZSiS5Cj2B3f18PBQtERtW5n4osbVP701WIm+kQ2xESfiIiI7EJDEwkAUMglCGunQrf2ZYl9kI8CUnYXJrK4InX9hsg0tjwRWRYTfSIiIrILDU0Meoc44Zl7/Rq99B4R1Z+zsmHDXhpanogsq2Ez0BARERFZSUMTg3ZtFEzyiZpIn1CnBpWP7Niw8kRkWUz0iYiIyC4wkSCyX6EBSnTwq315vXIhfkqE+NevLBFZBxN9IiIisgtMJIjsl0QiweOjfeCkrD19cFJK8dhoHy6tR2RjTPSJiIjILjCRILJvgd4KLJrSttYHco+MaINAb0UTRkVE1ZEIglD7YrWtWE5OEXQ6g63DICIialWSMzVYtzej2qX2QvyUeGy0DxMJIhsSBAEJaWqcvlqMS8kluJpa8bs6qKsLnhjja8PoiJovuVwKT09ni1yLiX4tmOgTERHZRuVEokith7NShsiOTgjxV7Iln8iOFJXq8fwnN6DRlaUUDjIJPpgTDFdHzrpP1FCWTPS5vB4RERHZHYlEgtAAFUIDVLYOhYhq4aySYWC4Cw79VQAA0OoFHDpfgHujPGwbGFErxzH6RERERETUaCN7uRltHziXD4OBnYaJbImJPhERERERNVp7XyU6ta2YoC8zX4fYxGIbRkRETPSJiIiIiMgsI3q6G23/ejbfRpEQEcBEn4iIiIiIzBTV2RnuThUT8P11vQSpORobRkTUujHRJyIiIiIis8hlEtwZ4Wq070AsW/XJPIIg4GpKKb45ko1P92XgmyPZuJpSCi4cVzcur1cLLq9HRERERFQ/2QU6zF9/A+Xz8DkppfhwTjCUDmxbpIZLztRg3d4MJKarTY518FPi8dE+CPRW2CAy67Hk8np281snCAKio6MxePBg9OrVC08//TSys7OrLXvq1ClMnDgRERERGDt2LI4cOSIemz59OsLCwkz+jRw5sqneChERERFRq+PlKkefUCdxu1htwPGLhTaMiJqr5EwNln6dUm2SDwCJ6Wos/ToFyZkcHlITu0n0169fj02bNmHx4sXYuHEjEhMT8fLLL5uUS0pKwpw5czBw4EBs374d/fr1wzPPPIPk5GQAwLvvvotffvnF6F/nzp0xduzYpn5LREREREStyshexpPy7Y/NZzdrahBBELBubwaK1bX3rC5WG7B+bwY/XzWwi0TfYDBgw4YNmDt3LoYPH44+ffpg4cKFOHz4MJKSkozKbt68GcHBwXjppZcQFhaGRYsWwd3dHTt27AAA+Pn5oX379uK/lJQU3Lx5E7NmzbLFWyMiIiIiajW6BKnQ1stB3L6RocGVlOpbZYmqE5+qrrElv6qEdDUS0vj5qo7c1gEAQFxcHHJycjBkyBBxX1RUFKRSKWJjYxEUFCTuP3nypFE5uVyOfv36ITY2ttprr1q1CtOmTYOHh0ejYpNIGnUaEREREVGrI5FIMLKXG744kCXu23c2D2GBKhtGRc3JmYTiBpU/HV+Mjm35+arKLhL98lb7ygm9SqWCl5cX0tLSTMoGBgYa7QsICMClS5dMrnv27FnExsbi/fffb1RclpoIgYiIiIiotRg/xAnbjuagRFPW9fqPq8WQKlXwcnWo40wiwIC8BpaXwdvbte6CrYxdJPpFRUWQSqVQKIxnTVSpVNBojCdYKC4uhkqlqrMcAHz11VcYPnw4/Pz8GhVXTk4R9HrOuk9ERERE1BC3d3HB/n+W19PpBew4mIr7B3raOCpqDqTQN7h8ZmaBlaJpWjKZ5Wbdt4tEX6FQwGAwQKfTQS6vCEmtVsPR0dGkrFarNdqn0WhMkv+8vDz89NNPWL16tVmxcW4HIiIiIqKGGdHTTUz0AeDAuXzcG+UBmZTjYql2fUKc8GNMbr3LR4Y6MWerhl1Mxlfe4l65m75Go0FOTo5Rd/7yslW786emppqUO3DgAJRKJQYOHGilqImIiIiIqDqB3gp0qTQuP6dQjzNXi2wYETUXoQFKBHjWb5hHiJ8SIf5KK0fUPNlFot+tWzeoVCocP35c3BcTEwOJRIK+ffsalY2MjMSxY8fEbb1ej5iYGJOE/tdff8Wdd94JBweOBSIiIiIiamojerkZbe+r1MJPVBt5PbJUJ6UUj432gYSzp1fLLhJ9lUqFqVOnYuXKlThy5AhOnz6NZcuWYcqUKXBzc8Ps2bPxyy+/AACmT5+O8+fPIzo6GnFxcViyZAkMBgMmTJhgdM1Tp04hMjLSFm+HiIiIiKjV6xPqDE9nmbh9MakUN7NM59Uiquz01WIkZWlrLRPip8SiKW0R6K2otVxrZhdj9AFg/vz5KC0txbx58yCTyTB+/HgsWLAAOp0OCQkJuHXrFgCga9euWLFiBVasWIG1a9eiR48e2LhxI1xcXMRr3bhxA/n5+ejataut3g4RERERUasml0kwrIcbdpzIEfftP5uPGSO8bRgV2TO9QcC2o9lG+8b2c4cEEhSp9XBWyhDZ0Qkh/kq25NdBIgicuqAmOTlF0Ok46z4RERERUWPkFurwwvobKF/ISuUgwYdz2sNRaRcdi8nO/HYuH5/uyxS3/TwcsPyRQMhlrSOpl8stN+s+f8OIiIiIiMgqPFzk6NepInEp1Qo4drFlLIVGlqXWGvBdpd4fADBpsGerSfItjYk+ERERERFZzYhe7kbb+87mg52Kqapf/sxHTpFe3A7xUxo9JKKGYaJPRERERERW07mtEkGVJk1LydbiYlKpDSMie1NYosfuU7lG+yYP8eI4fDMw0SciIiIiIquRSCQYWWWpvf1cao8q+SEmF8XqirnRetzmiK7BjjaMqPljok9ERERERFZ1excXOFWagO/01SJkF+hsGBHZi8x8HfadNX7wM2mwl42iaTmY6BMRERERkVUpHaQY3LViOWyDUDbDOtHOE9nQ6ivmbLg93AXtfZU2jKhlYKJPRERERERWN6JK9/2D5wug03NSvtYsOVODoxcKxW2ZFHhgkKcNI2o5mOgTEREREZHVBXgq0L19xbjrvGI9Tl0psmFEZGvbjmWj8gIMI3q6wcfdwXYBtSBM9ImIiIiIqElUnZRv39k8G0VCtnb5Zin+jC8Wt1UKCcb3Z2u+pTDRJyIiIiKiJtGrgxO83eTi9pUUNW5kqG0YEdmCIAj45kiW0b57Ij3g5iSzUUQtDxN9IiIiIiJqElKpBMN6VG3V56R8rc2fCcW4klLxgMfNSYYxke42jKjlYaJPRERERERN5s7urpBXarg9frEQRaV62wVETcpgELDtaLbRvvsHeEClYGpqSbybRERERETUZNycZOjfuWKpPY1OwJFKM69Ty3b0QiFuZmnFbV93OYZGuNVyBjUGE30iIiIiImpSVSfl2382HwaBS+21dBqtATuOG7fmPzjYC3KZxEYRtVxM9ImIiIiIqEmF+Ctxm59C3E7P1eLv6yU2jIiawr7YfGQXVgzTuM1XgajOzjaMqOViok9ERERERE1KIpHgrl7Gk69xUr6WrahUjx9O5hrtmzzEC1IJW/OtgYk+ERERERE1uf6dneGsqkhHziYUIyNPW8sZ1Jz9eCoXRWqDuN0t2BHd2zvZMKKWjYk+ERERERE1OYWDFHd2dxW3BQAHzrFVvyXKLtDhlzPGP9vJQ7xsFE3rwESfiIiIiIhsYkRPN1TuuH3ofAE0OkON5al52nkiB1p9xWSLA8Kc0cFPacOIWj4m+kREREREZBM+7g7o0aGi+3ZhqQExcUU2jIgsLSVbg8N/F4jbMinwwO1szbc2JvpERERERGQzVZfa46R8Lcu2o9movHLisAg3+Hk62C6gVoKJPhERERER2UzEbY7wdZeL2wlpaiSkldowIrKUKymlOH21WNxWOkhw3wAP2wXUijDRJyIiIiIim5FKJBjRk636LY0gCPjmSLbRvrsj3eHuLK/hDLIkJvpERERERGRTQ7q5QiGvmJbv5OUiFJTobRgRmSs2sQSXb1b0zHB1lOLuSA/bBdTKMNEnIiIiIiKbcnGUYWC4i7it1Qs4/FdBLWeQPTMYBGw7atyaf19/TzgqmX42Fd5pIiIiIiKyuRFVJuXbH5sPg0GooTTZs+OXCpGUqRG3fdzlGNbDrZYzyNKY6BMRERERkc3d5qtEx4CKtdUz83WIvVZcyxlkjzQ6A749lmO074HbveBQaWgGWR8TfSIiIiIisgtVl9rbz0n5mp0DsfnIKtCJ28E+CgwId7ZhRK0TE30iIiIiIrIL/Tq5wNWxIkU5d60E6TlaG0ZEDVGsNuD7k7lG+yYP8YJUwtb8psZEn4iIiIiI7IKDXIKhEVVa9c+xVb+52PNHLopKDeJ2lyAVIto72jCi1ouJPhERERER2Y1hPdxQuQH48F8FUGsNNZ9AdiG3UIefT+cZ7ZsyxAsStubbBBN9IiIiIiKyG95ucvQJdRK3i9UGnLhUaMOIqD52/p4Dja5ilYSozs4I8VfZMKLWjYk+ERERERHZlZG93I22953NhyBwqT17lZqjwaHzBeK2VAI8MMjThhERE30iIiIiIrIrXYNUCPByELdvZGhwNVVtw4ioNtuP5sBQ6TnM0AhXBHgqbBcQ2U+iLwgCoqOjMXjwYPTq1QtPP/00srOzqy176tQpTJw4ERERERg7diyOHDliUmb37t0YP348IiIiMGjQIPz+++/WfgtERERERGQBEokEI3oaT8q3j0vt2aX41FKculIkbivkEtw/gK35tmY3if769euxadMmLF68GBs3bkRiYiJefvllk3JJSUmYM2cOBg4ciO3bt6Nfv3545plnkJycLJbZtm0bFi1ahMmTJ+Pbb7/Ff//7X3h7ezfl2yEiIiIiIjMM7uoKpUPFRG4xcYXIK9LVcgY1NUEQ8M0R48bZMZHu8HCR2ygiKmcXib7BYMCGDRswd+5cDB8+HH369MHChQtx+PBhJCUlGZXdvHkzgoOD8dJLLyEsLAyLFi2Cu7s7duzYAQDIzc3F8uXL8frrr2PatGno3LkzBg8ejI4dO9rirRERERERUSM4KaUY1MVF3NYbgEN/FdRyBjW189dKcDG5VNx2UUlxT18P2wVEIrt41BIXF4ecnBwMGTJE3BcVFQWpVIrY2FgEBQWJ+0+ePGlUTi6Xo1+/foiNjQUA7N27F05OThg3bpxFYuNqEEREREREtjGytzsOnKtI7g/E5uPeKA/IpPySbmsGQcA3R41b88cP8ICzyi7akls9u0j0y1vtKyf0KpUKXl5eSEtLMykbGBhotC8gIACXLl0CAJw9exZhYWHYuHEjNm/eDLlcjrFjx+K5556Dg4MDGsLT07kxb4eIiIiIiCzA2xuI6JCD84llY8CzC/W4esuAQd09bBsY4cCfObiRoRG3fT0cMGVEOyjkTPTtgV0k+kVFRZBKpVAojGdmVKlU0Gg0RvuKi4uhUqlqLJeRkYGLFy/C19cX0dHRuHLlCpYuXQqFQoFnn322QXHl5BRBrzc04h0REREREZEl3NnNWUz0AWDHkXSE+ctsGBFpdQI+/TnFaN+EAR7Izy2q4QyqD5lMarHGZrtI9BUKBQwGA3Q6HeTyipDUajUcHR1Nymq1WqN9Go1GTP71ej2cnZ2xbNkySKVS9OjRA1evXsV3333X4EQfALhcJxERERGR7fQJdYansww5RXoAwIUbpUjO1KBdGy7fZisHYvORmV8xMWKQtwIDw12YO9kRu+hX4efnBwBG3fQ1Gg1ycnKMuvOXl63anT81NVUs5+XlhaCgIEilFW+tQ4cOyMrKslb4RERERERkJXKZBEN7GC+1tz+WS+3ZSonagO9P5hjtmzzYC1LOm2BX7CLR79atG1QqFY4fPy7ui4mJgUQiQd++fY3KRkZG4tixY+K2Xq9HTEwMBg4cCADo06cPLl68CLVaLZa5cuUKbrvtNuu+CSIiIiIisophEa6QVcpcjl4oQImGQ2xt4afTuSgoqbj3Ye1U6NHBsZYzyBbsItFXqVSYOnUqVq5ciSNHjuD06dNYtmwZpkyZAjc3N8yePRu//PILAGD69Ok4f/48oqOjERcXhyVLlsBgMGDChAkAgPHjx0Mmk+Hll1/G+fPnsW3bNnz99deYPXu2Ld8iERERERE1koeLHJEdK8Yul2oEHL9YaMOIWqe8Ih1+Op1ntG/KEC9IuFSZ3bGLRB8A5s+fj5EjR2LevHmYO3cuBg0ahJdffhk6nQ4JCQm4desWAKBr165YsWIFvv/+ezzwwAO4cuUKNm7cCBeXsjU2XV1dsWHDBty6dQtTp07FmjVr8O9//9tiy+0REREREVHTG9nLuPv+vrN5EDgovEl9/3su1NqKex7Z0Qkd26pqOYNsRSLwt6NGOTlF0OnYJYiIiIiIyNYEQcCrXyQjOatiYu7/TApAeBC7jTeF9BwtFn6ehPJFySQSYPkjgWjrxUkRLUUut9ys+3bTok9ERERERFQTiUSCkb3djfb9epaT8jWV7cezUXnl8Tu7uzLJt2NM9ImIiIiIqFm4PdwFjoqK8eCnrxYhu0BXyxlkCYnpapy8XCRuO8gkuH+Apw0jorow0SciIiIiomZBpZBiSDdXcdsgAAfPs1Xf2r45km20PbqPO7xc5TaKhuqDiT4RERERETUbI3oaT8r32/kC6PScdsxa/rpejL9vlIjbzkopxvZzr+UMsgdM9ImIiIiIqNkI8FKgW3DFBHx5RXr8cbWoljOosQyCYNKaP66/B5xVMhtFRPXFRJ+IiIiIiJqVqkvt7eekfFYRc7kI125pxG0vV5nJvSf7xESfiIiIiIialV4hTvByrWhVvnyzFDcy1DaMqOXR6QVsP2bcmj9xoBcUcqaQzQF/SkRERERE1KzIpBIM78FWfWs6eD4ft/IqVjRo18YBg7u62DAiaggm+kRERERE1OwMjXCDvNJQ8WMXC1FUqrddQC1IqcaA737PNdo3abAXpFJJ9SeQ3WGiT0REREREzY6bkwxRnStamDU6AUcvFNowopbj59N5yC+ueGjSqa0SvUOcbBgRNRQTfSIiIiIiapZGVllqb39sPgwCl9ozR36xHnv+yDXaN2VIG0gkbM1vTpjoExERERFRsxQaoMRtvgpxOy1HiwuV1nynhtt1Mgel2oqHJb1DndC5ncqGEVFjMNEnIiIiIqJmSSKRYESV5d72cVK+BhEEAVdTSvHNkWys2Z2OX/+suH8SCTBpkJcNo6PGkts6ACIiIiIiosYaEOaCrYeyUaQ2AAD+TChGZr4O3m5MdeqSnKnBur0ZSEyvfmnCPiFOCPRWVHuM7Bs//URERERE1GwpHaS4o7srfjqdBwAQBGDN7nQE+SjgrJShT6gTQgOUHGNeRXKmBku/TkHxPw9IqnMhqQTJmRom+82QRBA4W0VNcnKKoNPV/MEnIiIiIiLbS8/V4qWNSTUe7+CnxOOjfZiw/kMQBLz5VUqNLfmVhfgp8cZDbfmgpAnI5VJ4ejpb5Foco09ERERERM2aVidAVktmk5iuxtKvU5CcqWm6oOxYfKq6Xkk+ACSkq5GQVr+yZD+Y6BMRERERUbMlCALW7c2Avo6OuMVqA9bvzUBr7dCs1hpwLrEYWw5l4cNdaQ069/TVYitFRdbCMfpERERERNRsNaZ1OjSg5S8Xp9MLuJpaigs3SnAhqRTxqaV1PgypSZFab9ngyOqY6BMRERERUbN1Jr5hrc3rf8nA0Ag3dPBTor2vAkqHltHJ2WAQcD1DU5bY3yjB5Zul0Ogs03vBWSmzyHWo6TDRJyIiIiKiZquhrc03s7T48mAWgLJ14tt6OeA2PyU6/PMv2Kd5JP+CICAlW/tPi30JLiWViksMWlpkRyerXJesh4k+ERERERE1W+a0NgtCWeJ/M0uLYxcKAZQl/+28HNDBX4nbfCuSf4UdJP+Z+VpcuFEqJve5RfV/yOHnIUfXIEd0DXZEeKAK732XXu9Z90P8leaETTbA5fVqweX1iIiIiIjs29WUUizemmLV15BKgHZtFLjNTyG2/Af5KKCQ1z/5FwQB8alqnIkvRpFaD2elDH1CnRAaoKxx6br8Yj0uJJWI3fFv5enq/XoezjJ0DXb8J7lXwdvNweh4cqYGS79OQXEtvQCclFIsmtKWyxI2EUsur8dEvxZM9ImIiIiI7FtD1oTv4KfAzBHeuHZLg2vpaiTeUiMpQ9OoSepk0rLkv4Of8p+u/woEelef/CdnarBub0a1MXbwU+Lx0T4I9FagRG3ApeSSf5L7UiQ1YDlAJ6UUXYJU6BZc1mof4OlQ4wOE+sQV4qfEY//ERU2DiX4TYaJPRERERGT/zGmd1uoEJGdpkJiuLkv+09VIzmx88h/orSjr8v9P13+pBHh7e2qtsTnIJPD3lONmlhaGemZnCrkEYe1UZa32wY5o76OAVFp7Yl8dQRCQkKbG6asVPQ0iOzohxL/mngZkHUz0mwgTfSIiIiKi5sGSrdManQHJmRpcSy97AJCYrsbNrMYl/5YikwIdA1Riq31ogApyGRPxloSJfhNhok9ERERE1HxYs3VaozMgKaO85V8jJv/1bYFvKAmA9r4KscU+rJ2qWawGQI3HRL+JMNEnIiIiIqKaaLQGJGVWtPpfS9eYlfw7q6QYGOaCLsGO6BKogosj169vTZjoNxEm+kRERERE1BBqrQE3Msom+/v1bB7Scuo/U/6wHq6YNdLHitGRPbNkos++H0RERERERBaidJCiU1sV7urtjr4dXRp0rrOSLfhkGUz0iYiIiIiIrKBPqFODykd2bFh5opow0SciIiIiIrKC0AAlOvgp61U2xE+JEP/6lSWqCxN9IiIiIiIiK5BIJHh8tA+clLWnXU5KKR4b7cN168limOgTERERERFZSaC3AoumtK2xZT/ET4lFU9oi0FvRxJFRS2Y3s+4LgoDVq1dj69atKCwsxKBBg7BkyRJ4eXmZlD116hSWL1+OK1euIDg4GAsXLsSQIUPE40OHDkVqaqrROSdOnKj2WrXhrPtERERERGQJgiAgIU2N01eLUaTWw1kpQ2RHJ4T4K9mSTwBa6PJ669atw/r167F8+XJ4eHhg0aJFaNeuHdatW2dULikpCePHj8dDDz2E8ePHY8uWLdi5cyd2796NwMBAAEDv3r2xePFi9OjRQzwvKCgIUmnDOjAw0SciIiIiIqKm0OKW1zMYDNiwYQPmzp2L4cOHo0+fPli4cCEOHz6MpKQko7KbN29GcHAwXnrpJYSFhWHRokVwd3fHjh07AAAajQbFxcXo3Lkz2rdvL/5raJJPRERERERE1BzJbR0AAMTFxSEnJ8eo+31UVBSkUiliY2MRFBQk7j958qRROblcjn79+iE2NhYAkJeXBwDw9PS0SGzsRUNERERERETNiV0k+uWt9pUTepVKBS8vL6SlpZmULe+iXy4gIACXLl0CAOTm5gIARowYAQ8PD3Tr1g3z5s1DeHh4g+OyVLcJIiIiIiIioqZiF4l+UVERpFIpFArjmSZVKhU0Go3RvuLiYqhUqhrLBQQE4JtvvoFKpcLNmzfxySefYPr06fjhhx/g7+/foLhycoqg13OMPhEREREREVmXTGa5Mfp2kegrFAoYDAbodDrI5RUhqdVqODo6mpTVarVG+zQajZj8u7i4oGfPngCAsLAwREVFYdiwYfjxxx/x2GOPNTg2+5iqkIiIiIiIiKh+7CLR9/PzAwCkpaWJ3fI1Gg1ycnKMuvOXl63anT81NdWkXDkXFxcEBwcjJSWlwXHJZJzAj4iIiIiIiKzPkvmnXST63bp1g0qlwvHjxzF58mQAQExMDCQSCfr27WtUNjIyEseOHcOzzz4LANDr9YiJiamxtT4/Px/Xrl3DuHHjGhyXm5tj3YWIiIiIiIiI7IhdJPoqlQpTp07FypUrERAQACcnJyxbtgxTpkyBm5sbZs+ejSlTpmDUqFGYPn06Jk2ahOjoaIwaNQpfffUVDAYDJkyYAAA4ePAgEhMTERUVhfz8fKxatQrOzs7icSIiIiIiIqKWzC4SfQCYP38+SktLMW/ePMhkMowfPx4LFiyATqdDQkICbt26BQDo2rUrVqxYgRUrVmDt2rXo0aMHNm7cCBcXFwCAq6srvvnmG7z//vvw8PBAVFQU3n//fbi7u9vy7RERERERERE1CYkgcLo5IiIiIiIiopaCs80RERERERERtSBM9ImIiIiIiIhaECb6RERERERERC0IE30iIiIiIiKiFoSJPhEREREREVELwkSfiIiIiIiIqAVhok9ERERERETUgrT6RP/SpUt49NFH0bNnTwwaNAivvPIKcnJyxONfffUVhg8fjh49emDGjBlISkoyuUZWVhZeeOEFrFmzxmh/cnIynnjiCfTv3x99+/bF3LlzcfPmzXrFFRcXhwkTJiA8PBzh4eGIjIw0ie3LL79E//79ER4e3qSx7d27F/369UNYWBi6dOmChx56yCiu+fPno1u3bggLC0PPnj1NXttacdV2z/R6PRYtWoS+ffuKx++//367+nmWf9bCw8MRFhZmN7E9/PDDCAsLM/q3b9++Jomtrs/aV199hUGDBomx9+3bF7t27bLpPVu4cKHJ/QoLC0N4eHiT3LO6fp6xsbEYMWKEGNfIkSPt5rN2+vRpDB8+XIztnnvuabLYLl26hJkzZ6Jbt27o3r27ye/nxx9/jJ49eyIsLAzdu3fHihUrmiQuADh//jzuuusudOnSpdp6ylZ1QV2xbdmyBQMHDhQ//2PGjGlQbJXl5+ejf//+mDlzZr3isnYdak5s1qxDzYnL2nWotWIrZ04das3YzK1DzYnNmnWoOXFZuw61VmyA+XWoNWMztw41JzZr1qHmxAVYtw41NzaNRoM1a9bgscceMzm2ZcsWjB49GhERERgzZgy2bt1a7+uWa/WJ/pIlSxAVFYWvv/4ay5YtQ0xMDF5++WUAwE8//YTly5fjueeew1dffQWdToe5c+fCYDAAADIyMvB///d/GDVqFH755ReTa6empiIiIgLr16/HqlWrkJycjHnz5tUZU2FhIR599FGkp6fj4YcfxtSpU1FUVISjR4+Ksb3++utYsmQJCgoK4Ofn16Sxvfjii2jXrh0+/PBD3HvvvTh9+jTmzp0LANiwYQP27NmDqVOnYsWKFWjTpg0+/PBD7N6926b3TK/XIzExEaWlpXjmmWfQv39/XL58GY8//rhd/DzLP2ujRo2CIAgAYDefteTkZMhkMixYsADR0dHo3r073n//fZt/1n766ScsW7YMOTk5mDJlCrp06QJ3d3cEBQXZ9J699NJLWLRoERwcHMR75ubmBhcXF5v/PPPz8zFjxgykpKRg3rx5ePbZZ5GcnIyHH37Y5rGlpqZi5syZSE1NxbPPPosHH3wQ8fHxmDlzptVj0+v1ePrpp/HXX38BAEJDQ43qgt27d+ODDz5AcHAw/ve//6Ft27b45JNP8OOPP1o9rnXr1uGhhx5CcnIyunfvblJP2aouqCu2y5cvY+3atcjPz8e8efPw8MMPIzExEdOmTat3bJWtXbsWubm5dcYFNE0dak5s1qxDzYnL2nWotWIDzK9DrRmbuXWoObFZsw41Jy5r16HWis0Sdai1YrNEHdrY2Kxdh5oTl7XrUHNjGz16NNasWQOdTmd0/PLly/jqq6/w4osvYtu2bZg4cSLefPNN/PTTT/W6vkho5VJSUoy2d+3aJYSHhwvFxcXC/fffLyxZskQ8dvXqVaFz587CiRMnBEEQhIMHDwrTp08Xfv/9d2HYsGHC6tWra32tPXv2CJ07dxby8/NrLff5558LAwYMEK5fvy7umzRpkjB16lQxth49egj/+te/hPfee08YOXJkk8bWr18/Qa1Wi/tGjhwpdO7cWSguLhbGjRsnvPHGG+Kx8rimT59u9bjqumeVf55ZWVlC586d7ebnef/99wtvvvmmcNdddwnPPvusEB4ebjexde3aVXjiiSfE4/byWbvvvvuE3r17C6tWrWryuBryWUtISBC6du1qF7HFxMQInTt3Fl5//XXx+MyZM+0itmXLlgkRERFGf3OnTJnSJLEVFhYKI0eOFL755hthwYIFwiOPPGJUF4wYMUIIDw8XMjMzBUGo+KyNGzfO6nGNGTNG+OSTT8S4BMG4nrJVXVBXbKmpqcL48eMt8vO8cuWK0L9/f+HRRx8VX6c2TVmHNia2pqhDrXHPLFWHWis2S9Sh1ojNUnWopT9rlqpDrf1ZM6cOtXRslqxDLR2bJevQhsbWVHVoY+Jqqjq0sbFVvmeV5eTkCEVFRUb7Zs2aJbzwwgt1XruyVt+iHxAQYLStVCphMBiQn5+PCxcu4I477hCPhYaGwsfHB7GxsQCAO+64A1988QX69+9fr9cyGAxQKpVwdHSstdzJkycxYMAABAcHi/sGDBiAGzduiLGVlpZi7ty5kMvlTR7boEGDoFAoxH1hYWEAyrqsXL58GcOHDxePhYaGQqFQIDs72+px1XXPKv88y5/UeXh42MXP88KFC8jKykLHjh0xdOhQSCQSu/ms6XQ6o9e1l8/axYsXUVpaimnTpjV5XA35rK1ZswZjxoyxi9jK/95pNBpotVr89ddfuHz5sl38HsTFxUGtVhv9zR06dCikUqnVY3N2dsavv/6KSZMmQSKRADCuC5KSktC+fXu0adMGQNlnzdXVFVevXoUgCFaN66effsLjjz8uxlU1NlvVBXXFJpFIcOnSJaOfp4eHB5RKZYNiMxgMWLRoEZ588kn4+PjU6z00VR3a2NisXYda655Zog61Zmzm1qHWis0Sdag1PmuWqEOb4rPW2DrUGrFZqg61RmyWqkMbE1tT1KGNjasp6lBzYqt8zyrz8PCAk5OT0T6FQgG9Xl+v65dr9Yl+ZYIgYPv27ejZsyeysrIAAIGBgUZl2rZti7S0NACo9gdTHb1ej7///htr1qzB7NmzxQ9TTZKSkkxe19/fH1lZWXYXmyAIuHbtGiQSSbVxJScnQ6vVwsPDo0njAmq+ZykpKVi8eDGioqLQvn17u/l5Hj16FK+++qpYxh4+a+Vjdd99910MHDgQjzzyCP744w+7+az5+/vjwIEDGDlyJIYMGQK9Xi+OrbL1zzMwMBDp6enYs2cPHnnkEbv4eebn5wMAfvzxR0REROCBBx7A5MmT7eL3QKlUAjD++1FYWAhBEKweW1XV1QWVv1wBgLe3N/R6PXJycposrppis0Vd0NDYkpOTceLECXh7ezcoto8//hhqtVpMRuqjqepQS8RmjTrU2vfMnDrUmrGZW4daIzZL1aHW+qyZW4da+7NmTh1qjdgsVYdaIzZL1aGNia0qa9ShloirpthsURc0VHkdOmDAgAad1/CavIXSarV46623cPLkSWzevBnFxcUAYNKqoVKpoNFo6n3dRYsW4dtvv4XBYMB9992HOXPm1HlOcXExVCqVUWx79uyBwWDAa6+9Zjexld+z8oqjalyJiYl48skn4ezsLI75aoq4ymOr7p7de++90Ov1iIiIwPvvv49XX33V5j/P8j8mTz/9NNq1ayeWs4fPWlFREYCycUcSiQSbNm3CzJkzERYWZheftcLCQvzyyy/43//+h1u3buHFF19ETExMk8VVHltNv59bt25F586d0aNHD7v4eaakpAAA7rrrLjzyyCO4fPkyli9fjrZt29o8tuPHj+PgwYM4ePAgAgMDERsbi23btgGA1WOrzGAwICEhAfn5+UZ1QdUn6+V/5+obm7lxlcf22muv2UU91ZDYyuuCrl27Qi6X1zu206dPY/369fjmm28a9OChKepQS8RmjTrU2vfMnDrUWrFZog61VmyWqEOt+Vkzpw5tit/Pxtah1orNEnWotWKzRB3a2Ngqs0Ydaom4ymOzdB1qqdhqU7kOfeCBBxp0Llv0AaSlpWH69Ok4ePAgPv/8c0RERIhdnbRarVFZjUZTZ5fGyp577jns3LkTq1evRmpqKiZOnIjCwkIAQHR0NLp27Sr+i46OBlDWNaP8dctju3jxIlQqld3EVvmezZw5E46OjkZxlXfh6dChA0JCQuDs7GwX9+zjjz/GZ599Js4YnJOTY9OfZ5cuXbBq1SoAZRVHZfbwWSv/wxwcHIxBgwYhOjoagYGBSEtLs4vPmkajwXvvvYfevXtj9OjR8PPzQ1JSUr27Nlnzs1ZaWopt27Zh8uTJYqy2/nnu3bsXAPD888+jZ8+emDx5Mh5//HFcv37d5rENHDgQAPDee++hR48eePnll3HXXXdBLpdbPbZyaWlpOHLkCHJyckzqgqqVvlqtBgCjL17WigsASkpKcOHCBbupp+ob22+//SbWBevWrYNWq61XbGlpaXjuuefwyiuvoGPHjtWWsVUdaonYrFGHNsU9a2wdaq3YLFGHWvO+mVuHWvuz1tg6tCk+a42tQ60Zm7l1qDVjM7cONSe2ytewdB1qibgA69ShloqtNpXrgnXr1hkNxamPVt+in5iYiBkzZqBz5874/vvvxfEjfn5+AMpmJK7c3SQ1NRVjxoyp9/V9fX3h6+uL8PBwDB48GFFRUdi9ezemTJmChx56CHfffbdY1tPTU3zttLQ0o9geeOABnDx50iS2ypoqtitXrmDSpEniPdu0aROCgoLEuL744gts3boVL774ImbOnImhQ4eadIux1T1TKpXo378/Bg4ciGvXruH8+fMNejpm6djOnDmDCxcuAADuvvtuSKVSGAwG6PV6/Pnnn3B1dbWL+1b+eyCXy9G5c2fxibG1Y6vrs+bm5mb0lLioqAgGgwF5eXnw8vKy6T07fPgwMjMzMXLkSPEe2vpvR3nLV1paGtq3bw8A6NatG7Rarfi3z1axld+3tWvX4rbbbkNAQACWLFkCqVRq9c8aUFEXyOVy9OzZE7169TKKq+qScxkZGVAqlWKXamvFVR7bwYMHoVQqa6ynKmuKz1p9Y1u6dCleeuklzJw5ExKJpN6xbd++HZmZmVi8eDEWL14MAOKsxBEREdi4caPN6lBzY7NWHdoU96yxdai1YrNEHdpUn7XG1KHW/qw1tg5tinvW2DrUmrGZW4c2xX1rbB1qTmyA9epQc+Mqj80adaglYqvN5s2b8fbbb4t1QX2H4lXW6hP9F198Eb1798YHH3wAqbSig4Ofnx/atWuH48ePixMwJCYmIi0tTXxq1lASiQRSqVR8Uurl5VXtH9LIyEhs374dcXFxYmwPPfSQ+LqVYyvvJtKUsX388ccYNmwYPvzwQ0ilUvz+++8YOHAg/Pz84Ovri82bNyM6OhojR460y3tW/vPUaDQoLS21aWwRERHYs2cPZs2ahaFDh+KRRx7Bvn378MEHH8BgMODxxx+3WWyA6X3TarU4d+4c1Gq1zT9rbdq0QUZGBjIyMuDj44PExETk5OTA2dm5Xkm+OXHV55798MMP6NWrlxibPfweBAcH488//zT6PThx4gSAskl7bBlb+X2LiYnBoEGDUFBQgO+//75JPmtARV3g6OiI9PR0cb+fnx88PT1x5coVFBQUwNXVFYmJicjPz2+SuMpj8/LyQnBwsNGXSVvWBXXFVr5G8ciRIzFr1qwGx1b1yxFQ1lKVlZWFZcuWoW3btnB0dLRJHWpubNaqQ5vqnjWmDrVWbJaoQ5vyvjW0DrXmZ82cOrQp7llj61BrxmZuHdoU962xdag5sQHWq0PNjas8NmvUoZaIrSaXLl3C8uXL8eGHH4oPuxqjVSf6iYmJ+PvvvzFnzhxx8pFyXl5emDVrFt577z2Eh4cjMDAQy5cvx7Bhw8RZS+vywQcf4LbbbkN4eDgKCgrwySefQKlUYtSoUbWeN2nSJKxfvx6pqakYN24cli9fjosXL2L+/Pm4fv26UWx33HEH1Go1Xn311SaJbcCAAVi1ahWKiopw+PBhHDt2TIytoKAAnTp1QkZGBlJTU/Hrr7/i448/Rv/+/aFSqSAIQp1Po6x1z06ePInw8HB8+umncHZ2xpUrVxAbG4uePXva9Od569Yt+Pr64vHHH8d7772HgQMHQq1Ww2AwYNiwYYiKirJZbNevX0dqaipCQkLw6aefQqFQ4Pfff0d6ejoGDBhg88/aE088geXLl2PmzJl49NFHsX79esjl8nqPL7b27+eyZcswbNgwnD9/3m7+dowfPx47duzA+vXr4eLiArVajc8//xxBQUHo0qWLTWMzGAzo168fPvvsM0ilUuzbtw8SiQRDhw61+n2rXBf8+OOPKCkpwfXr1wGU1QVz5szBO++8g0cffRQzZszAypUrIZFI8MILL1g1rsqxRUVFobS0VIyrPDZb1QV1xbZz5054eHjg0KFD2LRpE/z9/cX6oHPnznVeu7oHDK6urigqKkJoaGit51q7DjUnNmvWoda8Z+bWodaKzRJ1qDXvm7l1qDU/a+bUoU3x+9nYOtSasZlbh1ozNnPrUHNis2Ydak5clWOzRh1qbmy12bNnD/z9/dGpUyejmIGyB071bd1v1Yl+ZmYmgLKxNlW99tprmDZtGrKzs/HWW29BrVZjxIgReP311+t9/Xbt2uHjjz9GSkoK3Nzc0K9fP3z99dfw9vau9Tw/Pz/Mnz8fy5Ytw9tvvy3unz59uklsGzduhFqtRlRUVJPEJggCAODYsWM4duyYUWyvvfYafH19IQgCli5danTeqFGjcObMmTrHGVrrns2cORMZGRnQ6/V47733IJFIxEqwvpri5/nWW2+hqKgIEokE77zzjs1je+qpp8T7tmrVKkilUtx5551NEltdn7UZM2YgKSkJW7duxX/+8x8olUrMmDGj3r0grPnznDx5MpYtW4aTJ0/i999/t6u/HZ988gleeeUVvPvuu5BIJOjUqRM+//xzm8f2/PPP4/z589BoNPjoo48gk8lw1113id3hrBlbdXVBeaL72muvYdasWbh27Rq2b9+Of//733B2dsbbb7+Nnj17WjWuyrGVT5BVOQG3ZV1QV2w9e/ZEbm4uAJjUB8XFxQ2at6WhmqIObSxr16GN1RR1qLVis0Qdaq3YLFGHNpa169DGaoo61JqxmVuHWis2S9ShjWXtOtQSsVmrDrWWjIwMJCcnV/vQvSF1gUQo/0tARERERERERM0eZ90nIiIiIiIiakGY6BMRERERERG1IEz0iYiIiIiIiFoQJvpERERERERELQgTfSIiIiIiIqIWhIk+ERERERERUQvCRJ+IiIiIiIioBWGiT0RERERERNSCMNEnIiIiIiIiakGY6BMREZHdGT58OKZPn27rMIiIiJolJvpERERkM8nJyfjss89sHQYREVGLwkSfiIiIbCY6OhrLly+3dRhEREQtChN9IiIiIiIiohaEiT4REVErsnDhQoSFheHmzZuYM2cOevXqhWHDhmHnzp0AgE8//RTDhg1D7969MWPGDCQkJBidv337djz44IPo1asXevbsiUmTJmH37t1GZXbs2IGwsDAcO3YM77//PgYPHozIyEg88cQTSEtLA1DWZT8sLEx83bCwMISFheHkyZNG1zp37hymTp2Knj17YuTIkdi4caO1bg0REVGLIREEQbB1EERERNQ0Fi5ciJ07d6JHjx4YN24c3Nzc8PHHHyMxMRETJ07E5cuXMWvWLKSmpiI6OhohISH47rvvAACLFy/Gl19+idGjR2PkyJHQaDTYtWsXTp48iYULF2LWrFkAyhL9V155BeHh4ejcuTOGDx+OCxcuYMOGDejduze+/PJLlJaWIiYmBp9++imOHz+OdevWAQB69OgBDw8PDB8+HBKJBHK5HNOnT4ebmxs2btyIixcvYu3atRg6dKiN7iAREZH9k9s6ACIiImp6M2bMwLhx4wAA7du3x7/+9S/88ssv2LdvHzw9PQEAKSkp2LJlC27evImsrCx8+eWXmDJlChYvXixe54EHHsDUqVPx4Ycf4sEHH4Srq6t4LCQkBP/73/8AAHfffTdSU1Pxww8/ID09HX5+frjjjjuwZ88eAMAdd9xhEmNqaip27NiB8PBwAECvXr1w11134ZdffmGiT0REVAt23SciImqF7rzzTvH/u3TpAgDo16+fmOQDQKdOnQAA6enp+O233wAAjzzyiNF1JBIJHnjgAZSUlODs2bNGx+6++26j7Z49ewIoe4BQHyEhIWKSDwDBwcHw8PAQu/8TERFR9ZjoExERtUJubm7i/6tUKgCAh4eHURlHR0cAgFarRUZGBgCgXbt2Jtfy9fUFAOTm5hrtd3d3N9p2dnYGAGg0mnrFWPmhQ+VraLXaep1PRETUWjHRJyIiojqVPwSorjX91q1bAABvb++mDImIiIhqwESfiIiI6jRkyBAAwBdffGG0XxAEfPvtt/D09ETv3r0bfF2FQgEAKC4uNj9IIiIiAsDJ+IiIiKge+vfvj3vvvRdffvklsrOzMXz4cGi1Wnz//feIjY3FihUrxCEADVE+D8CKFSvQr18/dO/eHYGBgZYOn4iIqFVhok9ERET18r///Q/du3fHt99+i3379kGpVKJnz574/PPPERUV1ahrTpo0CWfOnMHOnTvx448/4rPPPrNs0ERERK2QRBAEwdZBEBEREREREZFlcIw+ERERERERUQvCRJ+IiIiIiIioBWGiT0RERERERNSCMNEnIiIiIiIiakGY6BMRERERERG1IEz0iYiIiIiIiFoQJvpERERERERELQgTfSIiIiIiIqIWhIk+ERERERERUQvCRJ+IiIiIiIioBWGiT0RERERERNSCMNEnIiIiIiIiakH+Hzn1m820tfhNAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗 "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:29.324770Z",
     "start_time": "2025-06-10T02:57:29.073059Z"
    }
   },
   "source": [
    "# 检查数值型变量的缺失\n",
    "# 原始数据中-1作为缺失的标识，将-1替换为np.nan\n",
    "data1 = df1.drop(['ListingInfo','month'],axis=1)\n",
    "data1 = data1.replace({-1:np.nan})"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:32.418005Z",
     "start_time": "2025-06-10T02:57:30.553415Z"
    }
   },
   "source": [
    "# 缺失变量的数据可视化\n",
    "missing_df =sc.missing_cal(data1)\n",
    "missing_col = list(missing_df[missing_df.missing_pct>0].col)\n",
    "missingno.bar(data1.loc[:,missing_col])"
   ],
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The number of FixedLocator locations (0), usually from a call to set_ticks, does not match the number of labels (173).",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[9], line 4\u001B[0m\n\u001B[0;32m      2\u001B[0m missing_df \u001B[38;5;241m=\u001B[39msc\u001B[38;5;241m.\u001B[39mmissing_cal(data1)\n\u001B[0;32m      3\u001B[0m missing_col \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(missing_df[missing_df\u001B[38;5;241m.\u001B[39mmissing_pct\u001B[38;5;241m>\u001B[39m\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mcol)\n\u001B[1;32m----> 4\u001B[0m missingno\u001B[38;5;241m.\u001B[39mbar(data1\u001B[38;5;241m.\u001B[39mloc[:,missing_col])\n",
      "File \u001B[1;32mF:\\lqq\\Lib\\site-packages\\missingno\\missingno.py:266\u001B[0m, in \u001B[0;36mbar\u001B[1;34m(df, figsize, fontsize, labels, log, color, inline, filter, n, p, sort, ax)\u001B[0m\n\u001B[0;32m    264\u001B[0m ax3\u001B[38;5;241m.\u001B[39mset_xticks(ax1\u001B[38;5;241m.\u001B[39mget_xticks())\n\u001B[0;32m    265\u001B[0m ax3\u001B[38;5;241m.\u001B[39mset_xlim(ax1\u001B[38;5;241m.\u001B[39mget_xlim())\n\u001B[1;32m--> 266\u001B[0m ax3\u001B[38;5;241m.\u001B[39mset_xticklabels(nullity_counts\u001B[38;5;241m.\u001B[39mvalues, fontsize\u001B[38;5;241m=\u001B[39mfontsize, rotation\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m45\u001B[39m, ha\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mleft\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    267\u001B[0m ax3\u001B[38;5;241m.\u001B[39mgrid(\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m    269\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m ax \u001B[38;5;129;01min\u001B[39;00m axes:\n",
      "File \u001B[1;32mF:\\lqq\\Lib\\site-packages\\matplotlib\\axes\\_base.py:73\u001B[0m, in \u001B[0;36m_axis_method_wrapper.__set_name__.<locals>.wrapper\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m     72\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapper\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m---> 73\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m get_method(\u001B[38;5;28mself\u001B[39m)(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32mF:\\lqq\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:297\u001B[0m, in \u001B[0;36mrename_parameter.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    292\u001B[0m     warn_deprecated(\n\u001B[0;32m    293\u001B[0m         since, message\u001B[38;5;241m=\u001B[39m\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mold\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m parameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m() \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    294\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas been renamed \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnew\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m since Matplotlib \u001B[39m\u001B[38;5;132;01m{\u001B[39;00msince\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m; support \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    295\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfor the old name will be dropped %(removal)s.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    296\u001B[0m     kwargs[new] \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(old)\n\u001B[1;32m--> 297\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32mF:\\lqq\\Lib\\site-packages\\matplotlib\\axis.py:2025\u001B[0m, in \u001B[0;36mAxis.set_ticklabels\u001B[1;34m(self, labels, minor, fontdict, **kwargs)\u001B[0m\n\u001B[0;32m   2021\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(locator, mticker\u001B[38;5;241m.\u001B[39mFixedLocator):\n\u001B[0;32m   2022\u001B[0m     \u001B[38;5;66;03m# Passing [] as a list of labels is often used as a way to\u001B[39;00m\n\u001B[0;32m   2023\u001B[0m     \u001B[38;5;66;03m# remove all tick labels, so only error for > 0 labels\u001B[39;00m\n\u001B[0;32m   2024\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(locator\u001B[38;5;241m.\u001B[39mlocs) \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mlen\u001B[39m(labels) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(labels) \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m-> 2025\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   2026\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe number of FixedLocator locations\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   2027\u001B[0m             \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(locator\u001B[38;5;241m.\u001B[39mlocs)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m), usually from a call to\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   2028\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m set_ticks, does not match\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   2029\u001B[0m             \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m the number of labels (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(labels)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m).\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m   2030\u001B[0m     tickd \u001B[38;5;241m=\u001B[39m {loc: lab \u001B[38;5;28;01mfor\u001B[39;00m loc, lab \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(locator\u001B[38;5;241m.\u001B[39mlocs, labels)}\n\u001B[0;32m   2031\u001B[0m     func \u001B[38;5;241m=\u001B[39m functools\u001B[38;5;241m.\u001B[39mpartial(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_format_with_dict, tickd)\n",
      "\u001B[1;31mValueError\u001B[0m: The number of FixedLocator locations (0), usually from a call to set_ticks, does not match the number of labels (173)."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x1000 with 2 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:37.434743Z",
     "start_time": "2025-06-10T02:57:37.329984Z"
    }
   },
   "source": [
    "# 删除缺失率在80%以上的变量\n",
    "data1 = sc.missing_delete_var(df=data1,threshold=0.8)\n",
    "data1.shape"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失率超过0.8的变量个数为25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(49999, 203)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:39.238986Z",
     "start_time": "2025-06-10T02:57:38.982742Z"
    }
   },
   "source": [
    "# 样本的趋势个数可视化\n",
    "sc.plot_missing_user(df=data1,plt_size=(16,5))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:41.150282Z",
     "start_time": "2025-06-10T02:57:41.036598Z"
    }
   },
   "source": [
    "# 删除变量缺失个数在100个以上的用户\n",
    "data1 = sc.missing_delete_user(df=data1,threshold=100)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失变量个数在100以上的用户数有298个\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:57:47.472382Z",
     "start_time": "2025-06-10T02:57:42.368699Z"
    }
   },
   "source": [
    "# 同值化处理\n",
    "base_col = [x for x in data1.columns if x!='target']\n",
    "data1 = sc.const_delete(col_list=base_col,df=data1,threshold=0.9)\n",
    "data1 = data1.reset_index(drop=True)\n",
    "data1.shape"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "常变量/同值化处理的变量个数为56\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(49701, 147)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T02:58:39.800773Z",
     "start_time": "2025-06-10T02:58:36.374775Z"
    }
   },
   "source": [
    "# 保存数据至本地\n",
    "data1.to_csv('F:/安装包/PPD-First-Round-Data-Updated/PPD-First-Round-Data-Update/data1_clean.csv',encoding='gb18030',index=False)"
   ],
   "outputs": [],
   "execution_count": 14
  }
 ],
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   "language": "python",
   "name": "conda-base-py"
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